Foot Traffic Analysis: Analyse the city's foot traffic activities during the night time specifically (night economy). The purpose is to analyse/track the city's night time activity that associated with city plan to revive the night economy. ideally analyse the foot traffic on 1 hour interval.

Authored by: Te' Claire

Date: March-July 2024

Duration: 90 mins

Level: Intermediate

Pre-requisite Skills: Python, basic machine learning, Optional Google Collaborate access

Dataset 1: pedestrian-counting-system-sensor-locations 140 records
Dataset Link

Dataset 2: on-street-parking-bays 19162 records
Dataset Link

Dataset 3: pedestrian-counting-system-monthly-counts-per-hour 549976 records
Dataset Link

Dataset 4: pedestrian-network 85326 records
Dataset Link

Dataset 4: (5) Business establishments location and industry classification 374210 records
Dataset Link

Project Objective, Overview & Research

Context: To promote night economy by boosting local businesses and activities after hours in Melbourne City (7pm-7am). This use case examines pedestrian movement patterns during nighttime to support future planning and potential strategies that can be used for businesses in the area.

Objective: To track the city's nighttime foot traffic in order to identify active zones, time slots, and potential areas for development to stimulate the night economy.

• As a city council member, I want to analyze and understand the patterns of

nighttime foot traffic in Melbourne so that I can develop effective policies and initiatives to boost the Melbourne night scene and ensure safety and enjoyment for both residents and visitors.

• As a resident in Melbourne, I want to see the city's nighttime foot traffic so that I can identify areas which would suit my lifestyle, ultimately supporting local businesses.

Deliverables:

1. A detailed report containing the analysis of nighttime foot traffic, including graphical content.
2. Key findings and proposed strategies based on data.

Part 1

Data Preprocessing:

1. Combine datasets to identify pedestrian traffic (locations, counts, & network paths).
2. Clean (normalise) the data.

Temporal Analysis:

1. Outline pedestrian counts during hourly intervals nighttime (6 PM to 6 AM).
2. Identify peak activity times.

Part 2

Spatial Analysis: Map pedestrian counts using pedestrian network data to identify high vs low traffic areas (Pedestrian Counting System (sensors), (counts per hour) and Pedestrian Network)

Trend Analysis: Identity monthly and yearly analysis to find trends of nighttime foot traffic using sensors and pedestrain traffic. Additionally find parking locations that are nearby that would extend foot traffic areas (On Street Parking Bays).

Strategic Planning: Identifiy strategic locations for new businesses to assist in public transportation, (Business establishents location and industry classification).

---

Set up

# Dependencies
import warnings
warnings.filterwarnings("ignore")

import requests
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)

# Lib
!pip install geopandas
import time
import sys
from io import StringIO

from datetime import datetime, date
import numpy as np
from tqdm.auto import tqdm
import pandas as pd
import geopandas
import json
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
from shapely.geometry import Point

import geopandas as gpd
import folium
from folium.plugins import MarkerCluster
from scipy.spatial import cKDTree
from ipywidgets import interact_manual, Dropdown, Button, VBox

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from google.colab import drive
drive.mount('/content/drive')
with open('/content/drive/My Drive/SIT378/h.txt', 'r') as file:
    api_key = file.read().strip()

import os
api_key = os.getenv(api_key)

Mounted at /content/drive

# Define the company colors
color_d = ['#08af64', '#14a38e', '#0f9295', '#056b8a', '#121212'] #Dark theme
color_d_extend = ['#00918D', '#007E87', '#008883', '#0C5D70', '#51C293', '#008C9E', '#08af64', '#14a38e', '#0f9295', '#056b8a', '#121212']
color_night = ['#121212']
color_white = ['#B6B8D6']
color_l = ['#2af598', '#22e4ac', '#1bd7bb', '#14c9cb', '#0fbed8', '#08b3e5'] #Light theme
color_median = ['#9f1b04', '#7f10d0', '#cc8400', '#e480a1', '#4a71c7']

Load Dataset

• pedestrian-counting-system-monthly-counts-per-hour

# Download datasets
def download_dataset(api_key, dataset_id, base_url='https://data.melbourne.vic.gov.au/api/explore/v2.1/catalog/datasets/'):
    format = 'csv'
    url = f'{base_url}{dataset_id}/exports/{format}'
    params = {
        'select': '*',
        'limit': -1,  # all records
        'lang': 'en',
        'timezone': 'UTC',
        'api_key': api_key
    }

    with requests.get(url, params=params, stream=True) as response:
        if response.status_code == 200:
            total_size = int(response.headers.get('content-length', 0))
            chunk_size = 1024  # 1KB per chunk
            progress_bar = tqdm(total=total_size, unit='iB', unit_scale=True, desc=f"Downloading {dataset_id}")

            content = bytearray()
            for chunk in response.iter_content(chunk_size=chunk_size):
                if chunk:  # filter out keep-alive new chunks
                    content.extend(chunk)
                    progress_bar.update(len(chunk))

            progress_bar.close()
            data = pd.read_csv(StringIO(content.decode('utf-8')), delimiter=';')
            return data
        else:
            print(f'Request failed with status code {response.status_code}')
            return None

# Dataset IDs
dataset_ids = [
    'pedestrian-counting-system-monthly-counts-per-hour',
    'on-street-parking-bays',
    'pedestrian-network',
    'pedestrian-counting-system-sensor-locations',
    'business-establishments-with-address-and-industry-classification'
]

# Initialize dictionary to hold the datasets
datasets = {}

# Download each dataset with a progress bar
for dataset_id in dataset_ids:
    datasets[dataset_id] = download_dataset(api_key, dataset_id)
    if datasets[dataset_id] is not None:
        print(f"{dataset_id} downloaded successfully.")

Downloading pedestrian-counting-system-monthly-counts-per-hour: 0.00iB [00:00, ?iB/s]

pedestrian-counting-system-monthly-counts-per-hour downloaded successfully.

Downloading on-street-parking-bays: 0.00iB [00:00, ?iB/s]

on-street-parking-bays downloaded successfully.

Downloading pedestrian-network: 0.00iB [00:00, ?iB/s]

pedestrian-network downloaded successfully.

Downloading pedestrian-counting-system-sensor-locations: 0.00iB [00:00, ?iB/s]

pedestrian-counting-system-sensor-locations downloaded successfully.

Downloading business-establishments-with-address-and-industry-classification: 0.00iB [00:00, ?iB/s]

business-establishments-with-address-and-industry-classification downloaded successfully.

View Datasets

print(datasets['pedestrian-counting-system-monthly-counts-per-hour'].head())

  sensor_name                  timestamp  locationid  direction_1  \
0    RMIT14_T  2023-05-03T09:00:00+00:00          61          294   
1    RMIT14_T  2023-05-03T13:00:00+00:00          61           78   
2    Lat224_T  2023-05-02T14:00:00+00:00          62           63   
3    Lat224_T  2023-05-02T20:00:00+00:00          62           50   
4    Lat224_T  2023-05-02T21:00:00+00:00          62           61   

   direction_2  total_of_directions                    location  
0          379                  673  -37.80767455, 144.96309114  
1           64                  142  -37.80767455, 144.96309114  
2           46                  109  -37.80996494, 144.96216521  
3           27                   77  -37.80996494, 144.96216521  
4           42                  103  -37.80996494, 144.96216521  

print(datasets['on-street-parking-bays'].head())

   roadsegmentid kerbsideid  \
0          20751       8598   
1          20751       8604   
2          20751       8607   
3          22499       8624   
4          22499       8625   

                              roadsegmentdescription   latitude   longitude  \
0  Argyle Place South between Lygon Street and Ca... -37.803448  144.965682   
1  Argyle Place South between Lygon Street and Ca... -37.803428  144.965496   
2  Argyle Place South between Lygon Street and Ca... -37.803413  144.965361   
3  Queensberry Street between Lygon Street and Ca... -37.804688  144.965314   
4  Queensberry Street between Lygon Street and Ca... -37.804695  144.965379   

  lastupdated  
0  2023-05-28  
1  2023-05-28  
2  2023-05-28  
3  2023-05-28  
4  2023-05-28  

print(datasets['pedestrian-network'].head())

                          geo_point_2d  \
0  -37.791128630450004, 144.9250621319   
1    -37.790885197, 144.92478186034998   
2  -37.79063325525, 144.92244485650002   
3  -37.790601697949995, 144.9253671047   
4         -37.790968201, 144.942393422   

                                           geo_shape  objectid  neworkid  
0  {"coordinates": [[144.9251354935, -37.79109699...      5735       NaN  
1  {"coordinates": [[144.9247703611, -37.79089170...      5738       NaN  
2  {"coordinates": [[144.9224965319, -37.79068385...      5740       NaN  
3  {"coordinates": [[144.9254894579, -37.79073841...      5743       NaN  
4  {"coordinates": [[144.9424411919, -37.79097389...      5744       NaN  

print(datasets['pedestrian-counting-system-sensor-locations'].head())

   location_id                      sensor_description sensor_name  \
0           24           Spencer St-Collins St (North)    Col620_T   
1           25  Melbourne Convention Exhibition Centre      MCEC_T   
2           36                         Queen St (West)     Que85_T   
3           37                         Lygon St (East)    Lyg260_T   
4           41          Flinders La-Swanston St (West)       Swa31   

  installation_date                         note location_type status  \
0        2013-09-02                          NaN       Outdoor      A   
1        2013-08-28                          NaN       Outdoor      A   
2        2015-01-20  Pushbox Upgrade, 03/08/2023       Outdoor      A   
3        2015-02-11  Pushbox Upgrade, 30/06/2023       Outdoor      A   
4        2017-06-29                          NaN       Outdoor      A   

  direction_1 direction_2   latitude   longitude                    location  
0        East        West -37.818880  144.954492  -37.81887963, 144.95449198  
1        East        West -37.824018  144.956044  -37.82401776, 144.95604426  
2       North       South -37.816525  144.961211  -37.81652527, 144.96121062  
3       North       South -37.803103  144.966715  -37.80310271, 144.96671451  
4       North       South -37.816686  144.966897  -37.81668634, 144.96689733  

print(datasets['business-establishments-with-address-and-industry-classification'].head())

   census_year  block_id  property_id  base_property_id  clue_small_area  \
0         2004        44       108110            108110  Melbourne (CBD)   
1         2004        44       108110            108110  Melbourne (CBD)   
2         2004        44       108111            108111  Melbourne (CBD)   
3         2004        44       108111            108111  Melbourne (CBD)   
4         2004        44       108111            108111  Melbourne (CBD)   

              trading_name                                   business_address  \
0  Total Tel International   Part Level 12, 140-0 Queen Street MELBOURNE 3000   
1          CJ Ham & Murray      Part Floor 2, 140 Queen Street MELBOURNE 3000   
2    Netherlands Consulate  Suite 7, Part Level 4, 118-126 Queen Street ME...   
3       Thomas Koutsoupias  Suite 10, Level 9, 118-126 Queen Street MELBOU...   
4           K S T Partners  Suite 4, Floor 5, 118 Queen Street MELBOURNE 3000   

   industry_anzsic4_code       industry_anzsic4_description   longitude  \
0                   5809  Other Telecommunications Services  144.961185   
1                   6720               Real Estate Services  144.961185   
2                   7552  Foreign Government Representation  144.961308   
3                   6931                     Legal Services  144.961308   
4                   6932                Accounting Services  144.961308   

    latitude  
0 -37.815391  
1 -37.815391  
2 -37.815656  
3 -37.815656  
4 -37.815656  

A. Save datasets locally

# Save
base_path = '/content/drive/My Drive/sit378_foot_traffic_analysis/'

for dataset_id, df in datasets.items():
    if df is not None:
        filename = f"{base_path}{dataset_id}.csv"
        df.to_csv(filename, index=False)
        print(f"Saved {filename} to Google Drive.")

Saved /content/drive/My Drive/sit378_foot_traffic_analysis/pedestrian-counting-system-monthly-counts-per-hour.csv to Google Drive.
Saved /content/drive/My Drive/sit378_foot_traffic_analysis/on-street-parking-bays.csv to Google Drive.
Saved /content/drive/My Drive/sit378_foot_traffic_analysis/pedestrian-network.csv to Google Drive.
Saved /content/drive/My Drive/sit378_foot_traffic_analysis/pedestrian-counting-system-sensor-locations.csv to Google Drive.
Saved /content/drive/My Drive/sit378_foot_traffic_analysis/business-establishments-with-address-and-industry-classification.csv to Google Drive.

B. Load datasets

# Load
base_path = '/content/drive/My Drive/sit378_foot_traffic_analysis/'
datasets = {} # Dictionary

# Filenames
dataset_filenames = {
    'pedestrian_hour': 'pedestrian-counting-system-monthly-counts-per-hour.csv',
    'on_street_parking': 'on-street-parking-bays.csv',
    'pedestrian-network': 'pedestrian-network.csv',
    'pedestrian-counting-system-sensor-locations': 'pedestrian-counting-system-sensor-locations.csv',
    'business-establishments-with-address-and-industry-classification': 'business-establishments-with-address-and-industry-classification.csv'
}

# Load each dataset into the datasets dictionary
for dataset_id, filename in dataset_filenames.items():
    full_path = f"{base_path}{filename}"
    datasets[dataset_id] = pd.read_csv(full_path)
    print(f"Loaded {dataset_id} with {datasets[dataset_id].shape[0]} records.")

Loaded pedestrian_hour with 549976 records.
Loaded on_street_parking with 19162 records.
Loaded pedestrian-network with 85326 records.
Loaded pedestrian-counting-system-sensor-locations with 140 records.
Loaded business-establishments-with-address-and-industry-classification with 374210 records.

# Dataframes
pedestrian_hour = datasets['pedestrian_hour']
on_street_parking = datasets['on_street_parking']
pedestrian_network = datasets['pedestrian-network']
sensor_locations = datasets['pedestrian-counting-system-sensor-locations']
business_establishments = datasets['business-establishments-with-address-and-industry-classification']

Data Preprocessing:

1. Combine datasets to identify pedestrian traffic (locations, counts, & network paths).
2. Clean (normalise) the data.

# Check missing values for dataset
print('pedestrian_network')
print(pedestrian_network.isnull().sum())
print("#" * 30)
print()

print('on_street_parking')
print(on_street_parking.isnull().sum())
print("#" * 30)
print()

print('pedestrian_hour')
print(pedestrian_hour.isnull().sum())
print("#" * 30)
print()

print('sensor_locations')
print(sensor_locations.isnull().sum())
print("#" * 30)

print('business_establishments')
print(business_establishments.isnull().sum())
print("#" * 30)

pedestrian_network
geo_point_2d        0
geo_shape           0
objectid            0
neworkid        71060
dtype: int64
##############################

on_street_parking
roadsegmentid                 0
kerbsideid                14149
roadsegmentdescription        0
latitude                      0
longitude                     0
lastupdated                   0
dtype: int64
##############################

pedestrian_hour
sensor_name            0
timestamp              0
locationid             0
direction_1            0
direction_2            0
total_of_directions    0
location               0
dtype: int64
##############################

sensor_locations
location_id             0
sensor_description      0
sensor_name             0
installation_date       2
note                  107
location_type           0
status                  0
direction_1            43
direction_2            43
latitude                0
longitude               0
location                0
dtype: int64
##############################
business_establishments
census_year                        0
block_id                           0
property_id                        0
base_property_id                   0
clue_small_area                    0
trading_name                     127
business_address                   1
industry_anzsic4_code              0
industry_anzsic4_description       0
longitude                       4785
latitude                        4785
dtype: int64
##############################

Data Cleaning

• Find missing data
  • pedestrian_network 85326 | missing 71060 network ids = 83.28% drop
  • on_street_parking 19162 | misisng 14149 kerbsideid = 73.84% drop
  • pedestrian_hour no NaNs
  • sensor 140 | missing note 107 = 76.43% drop
  • sensor 140 | missing direction_1 43 & direction_2 43 = 30.71% (attempt to clean)
  • business_locations 374210 | missing long/ lat 4785 = 1.28% drop
  • business_locations 374210 | missing trading_name 127 = 0.03% drop
• Merge dataframes (pedestrian hour and sensor locations)
• Creat a GeoDataFrame for pedestrian Network

# pedestrian_network drop
pedestrian_network = pedestrian_network.drop(columns=['neworkid'])
# on_street_parking drop
on_street_parking = on_street_parking.drop(columns=['kerbsideid'])
# sensor drop
sensor = sensor_locations.drop(columns=['note', 'installation_date'])

# Business establishments drop
business_establishments.dropna(subset=['business_address', 'trading_name', 'longitude', 'latitude'], inplace=True)

# Merge sensor_name
pedestrian_hour_sensor = pd.merge(pedestrian_hour, sensor_locations, on='sensor_name', how='left')
print(pedestrian_hour_sensor.head())

  sensor_name                  timestamp  locationid  direction_1_x  \
0    RMIT14_T  2023-05-03T09:00:00+00:00          61            294   
1    RMIT14_T  2023-05-03T13:00:00+00:00          61             78   
2    Lat224_T  2023-05-02T14:00:00+00:00          62             63   
3    Lat224_T  2023-05-02T20:00:00+00:00          62             50   
4    Lat224_T  2023-05-02T21:00:00+00:00          62             61   

   direction_2_x  total_of_directions                  location_x  \
0            379                  673  -37.80767455, 144.96309114   
1             64                  142  -37.80767455, 144.96309114   
2             46                  109  -37.80996494, 144.96216521   
3             27                   77  -37.80996494, 144.96216521   
4             42                  103  -37.80996494, 144.96216521   

   location_id   sensor_description installation_date note location_type  \
0         61.0     RMIT Building 14        2019-06-28  NaN       Outdoor   
1         61.0     RMIT Building 14        2019-06-28  NaN       Outdoor   
2         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
3         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
4         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   

  status direction_1_y direction_2_y   latitude   longitude  \
0      A         North         South -37.807675  144.963091   
1      A         North         South -37.807675  144.963091   
2      A          East          West -37.809965  144.962165   
3      A          East          West -37.809965  144.962165   
4      A          East          West -37.809965  144.962165   

                   location_y  
0  -37.80767455, 144.96309114  
1  -37.80767455, 144.96309114  
2  -37.80996494, 144.96216521  
3  -37.80996494, 144.96216521  
4  -37.80996494, 144.96216521  

# split geo_point in lat/long
pedestrian_network = pd.DataFrame(pedestrian_network)
pedestrian_network[['lat', 'lon']] = pedestrian_network['geo_point_2d'].str.split(',', expand=True)
# print(pedestrian_network)

# GeoDataFrame
geometry = [Point(lon, lat) for lon, lat in zip(pedestrian_network['lon'], pedestrian_network['lat'])]
gpd_pedestrianpath = gpd.GeoDataFrame(pedestrian_network, geometry=geometry, crs="EPSG:4326")
gpd_pedestrianpath.drop(columns=['lat', 'lon'], inplace=True)
# print(gpd_pedestrianpath)

Overview Pedestrian Paths

• Overview of day and night data
  • Review specific sensor points (ACMI)
  • Review all sensor locations

# Specific location traffic ACMI
pedestrian_hour_sensor['timestamp'] = pd.to_datetime(pedestrian_hour_sensor['timestamp'])
specific_sensor_data_acmi_day_night = pedestrian_hour_sensor[pedestrian_hour_sensor['sensor_name'] == 'ACMI_T']
specific_sensor_data_acmi_day_night = specific_sensor_data_acmi_day_night.groupby('timestamp')['total_of_directions'].sum()

# Plot
plt.figure(figsize=(10, 5))
plt.plot(specific_sensor_data_acmi_day_night.index, specific_sensor_data_acmi_day_night.values, color=color_d[3], alpha=0.7)
plt.axhline(specific_sensor_data_acmi_day_night.mean(), color='purple', linestyle='--', label='Avg ACMI_T Traffic')
plt.title('Traffic Over Time for Sensor ACMI_T')
plt.xlabel('Time')
plt.ylabel('Total Traffic')
plt.grid(True)
plt.show()

specific_sensor_data_acmi_day_night.describe()

count    6950.000000
mean      405.964604
std       377.874285
min         1.000000
25%        59.000000
50%       347.500000
75%       636.000000
max      5331.000000
Name: total_of_directions, dtype: float64

# Group data (Sensor and timestamp + traffic)
grouped_data_day_night = pedestrian_hour_sensor.groupby(['sensor_name', 'timestamp'])['total_of_directions'].sum().unstack(0)

# Multiple sensors
plt.figure(figsize=(12, 7))
for sensor in grouped_data_day_night.columns:
    plt.plot(grouped_data_day_night.index, grouped_data_day_night[sensor], label=sensor)

plt.title('Traffic Over Time by Sensor')
plt.xlabel('Time')
plt.ylabel('Total Traffic')
plt.legend(title='Sensor Name', loc='upper left', bbox_to_anchor=(1.05, 1))

plt.grid(True)
plt.tight_layout()
plt.show()

grouped_data_day_night.describe()

sensor_name	261Will_T	280Will_T	474Fl_T	488Mac_T	574Qub_T	ACMI_T	AG_T	AlfPl_T	BirBridge_T	BirFed1120_T	Bou231_T	Bou283_T	Bou292_T	Bou655_T	Bou688_T	Bou892T	BouBri_T	BouHbr2353_T	BouHbr_T	Boyd2837_T	Col12_T	Col15_T	Col254_T	Col620_T	Col623_T	Col700_T	Col892T	ElFi_T	Eli250_T	Eli263_T	Eli380_T	Eli483_T	Eli501_T	Errol20_T	Errol23_T	FLDegC_T	FLDegN_T	FLDegS_T	FedCycle_T	FedPed_T	Fli114C_T	Fli114F_T	FliSS_T	FliS_T	Fra118_T	Grat292_T	Hammer1584_T	HarEsB_T	HarEsP_T	KenMac_T	King2_T	King335_T	Lat224_T	LatWill_T	Lon189_T	Lon364_T	LtB170_T	LtB210_T	Lyg161_T	Lyg260_T	Lyg309_T	MCEC_T	NewQ_T	Pel147_T	PriNW_T	QVMQ_T	QVN_T	Que85_T	RMIT14_T	RMIT_T	Rus180_T	SanBri_T	Signal1647_T	SouthB_T	Spen161_T	Spen201_T	Spen229_T	Spr201_T	SprFli_T	Swa123_T	Swa148_T	Swa295_T	Swa31	Swa607_T	SwaCs_T	UM1_T	UM2_T	UM3_T	VAC_T	Vic_T	WatCit_T	WebBN_T	WestWP_T
count	6687.000000	6184.000000	2651.000000	6682.000000	6757.000000	6950.000000	7445.000000	6241.000000	3316.000000	2575.000000	6926.000000	7187.000000	7033.000000	462.000000	7484.000000	569.000000	4606.000000	2457.000000	7159.000000	1546.000000	6559.000000	7464.000000	7462.000000	7414.000000	6920.000000	7014.000000	1211.000000	7004.000000	6939.000000	6928.000000	5698.000000	2736.000000	6553.000000	7287.000000	7269.000000	6963.000000	6490.000000	7502.000000	5932.000000	5898.000000	6269.000000	7346.000000	7289.000000	7513.000000	6829.000000	435.000000	2844.000000	6772.000000	7124.000000	6738.000000	2827.000000	2860.000000	7427.000000	7210.000000	6855.000000	7337.000000	6836.000000	6730.000000	6916.000000	5241.000000	6854.000000	4414.000000	7469.000000	5842.000000	6468.000000	6594.000000	7436.000000	7117.000000	7282.000000	7511.000000	6489.000000	7382.000000	2349.000000	7243.000000	2861.000000	2815.000000	2861.000000	7135.000000	6086.000000	6994.000000	7160.000000	7515.000000	7472.000000	6951.000000	7235.000000	2073.000000	4293.000000	5007.000000	7297.000000	7142.000000	7024.000000	7190.000000	6857.000000
mean	405.031554	145.880821	197.830630	93.821610	56.637561	405.964604	348.342243	127.767505	39.319662	189.899417	419.493070	621.212745	1029.900754	216.751082	633.533137	40.920914	454.746852	55.475376	98.500629	142.347995	218.294405	362.817256	558.034575	830.636903	317.745087	466.085258	29.752271	1182.510137	913.928952	389.096276	1079.430151	456.206871	253.499161	162.296693	110.055991	165.005170	194.966256	466.077446	70.864295	248.417430	30.613814	230.622380	625.592400	852.287901	131.679748	175.501149	590.021449	63.215741	177.753930	67.045562	158.435798	170.997552	228.006598	206.950485	425.607877	287.292899	326.314950	541.796137	199.646906	109.136424	224.931719	670.590847	222.686705	93.192229	1165.311843	245.925842	1099.181818	224.142897	430.894946	724.985621	502.158114	370.187212	97.080885	1036.972663	493.621111	467.793961	670.765816	216.667134	73.726914	1488.440520	678.308520	1199.210379	1705.917827	186.873687	566.151486	183.835986	146.238994	142.372678	722.608058	136.041865	111.940632	200.096245	25.356278
std	485.143904	119.553327	133.239904	75.655576	46.290549	377.874285	457.730530	129.570908	60.352792	369.432044	317.611887	687.371979	1070.424036	196.006709	522.747853	34.232105	901.958827	63.853127	108.192349	64.483945	282.589181	391.112846	540.030877	679.976485	301.395793	692.355900	23.689096	897.054660	749.464920	304.980070	786.829308	382.157033	229.062175	135.060428	93.371579	167.479003	172.956293	380.753253	81.348463	532.749304	25.876258	211.831268	473.330632	673.099758	103.250481	253.103878	612.136901	89.192001	180.779591	54.838090	117.788533	123.354520	160.970999	194.081081	319.310837	224.216332	308.146188	480.042581	185.554694	121.354154	201.667438	640.030338	255.124785	91.367629	1026.952150	253.494147	1004.418646	189.143552	386.177077	724.689877	401.832270	301.606842	97.471698	842.000843	417.407065	327.527708	451.525393	202.739631	61.797717	1330.923215	594.778154	971.001614	1300.803204	172.244378	494.609776	222.579035	189.299122	153.457336	697.376112	121.383867	124.540049	184.142205	24.552942
min	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	8.000000	8.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	1.000000	2.000000	1.000000	3.000000	2.000000	1.000000	1.000000	1.000000	1.000000	3.000000	1.000000	1.000000	1.000000	1.000000	1.000000
25%	69.000000	30.000000	72.000000	14.000000	10.000000	59.000000	42.000000	23.000000	4.000000	22.000000	108.250000	47.000000	72.000000	47.000000	131.000000	19.000000	30.250000	9.000000	13.000000	109.000000	13.000000	52.000000	50.000000	182.000000	54.000000	31.000000	12.000000	252.000000	168.000000	62.000000	269.000000	122.000000	79.000000	12.000000	13.000000	13.000000	31.000000	62.000000	15.000000	19.000000	10.000000	42.000000	157.000000	188.000000	31.000000	9.000000	65.000000	8.000000	22.000000	11.000000	53.000000	45.000000	87.000000	39.000000	118.000000	60.000000	53.000000	65.000000	24.000000	12.000000	15.000000	89.000000	33.000000	17.000000	165.000000	43.000000	103.000000	48.000000	67.000000	114.500000	101.000000	91.000000	20.000000	223.000000	101.000000	144.500000	183.000000	27.000000	27.000000	178.000000	98.000000	218.000000	336.750000	36.000000	90.000000	23.000000	15.000000	24.000000	63.000000	27.000000	22.000000	21.000000	7.000000
50%	224.000000	133.000000	205.000000	97.000000	54.000000	347.500000	222.000000	86.000000	12.000000	109.000000	395.500000	330.000000	574.000000	169.500000	561.000000	35.000000	172.500000	29.000000	65.000000	149.000000	86.000000	216.000000	387.500000	775.000000	253.000000	136.500000	26.000000	1194.500000	831.000000	381.000000	1018.500000	399.000000	199.000000	171.000000	105.000000	98.000000	149.500000	459.000000	50.000000	88.000000	27.000000	212.000000	632.000000	837.000000	124.000000	67.000000	452.500000	42.000000	156.000000	64.000000	154.000000	188.000000	222.000000	161.000000	401.000000	275.000000	257.500000	424.500000	174.500000	73.000000	208.000000	526.000000	182.000000	68.000000	1016.000000	185.500000	817.000000	194.000000	382.000000	561.000000	445.000000	357.000000	82.000000	951.000000	410.000000	487.000000	767.000000	168.000000	69.000000	1153.000000	563.000000	991.000000	1749.000000	148.000000	516.000000	98.000000	77.000000	90.000000	569.000000	120.000000	90.000000	171.500000	17.000000
75%	609.000000	235.000000	296.000000	155.000000	92.000000	636.000000	491.000000	200.000000	54.000000	212.500000	660.750000	1113.000000	1976.000000	334.000000	978.000000	52.000000	552.750000	84.000000	150.000000	185.000000	382.000000	572.000000	1007.000000	1222.750000	469.000000	678.000000	41.000000	1915.000000	1575.000000	660.000000	1756.750000	654.000000	332.000000	272.000000	184.000000	288.000000	330.000000	791.750000	87.000000	194.000000	43.000000	345.000000	975.000000	1292.000000	207.000000	198.000000	929.750000	95.000000	273.000000	114.000000	235.000000	266.000000	345.000000	307.000000	671.000000	470.000000	514.000000	963.000000	309.000000	173.000000	387.000000	1078.000000	322.000000	139.000000	1872.500000	335.000000	1996.000000	346.000000	677.000000	1089.000000	856.000000	546.000000	141.000000	1616.500000	818.000000	694.000000	1029.000000	356.000000	105.000000	2672.000000	1132.250000	2089.000000	2835.000000	283.000000	912.000000	250.000000	189.000000	213.000000	1171.000000	201.000000	161.000000	338.000000	37.000000
max	2813.000000	1023.000000	907.000000	340.000000	241.000000	5331.000000	5926.000000	890.000000	608.000000	5680.000000	1658.000000	3791.000000	5188.000000	1053.000000	2997.000000	274.000000	10387.000000	518.000000	1898.000000	316.000000	1676.000000	1866.000000	2368.000000	3518.000000	2595.000000	4137.000000	158.000000	4943.000000	3047.000000	1105.000000	3237.000000	2217.000000	2252.000000	915.000000	429.000000	829.000000	772.000000	1413.000000	548.000000	6663.000000	290.000000	3316.000000	3217.000000	7367.000000	614.000000	905.000000	6908.000000	1909.000000	2433.000000	255.000000	1161.000000	1433.000000	3692.000000	1592.000000	2792.000000	1061.000000	2241.000000	2135.000000	1158.000000	868.000000	915.000000	4960.000000	5782.000000	519.000000	6271.000000	2372.000000	4132.000000	935.000000	3394.000000	5269.000000	2814.000000	3513.000000	1173.000000	8526.000000	3858.000000	1981.000000	2150.000000	1184.000000	985.000000	5963.000000	3878.000000	4905.000000	5535.000000	1030.000000	3151.000000	1127.000000	1721.000000	1683.000000	4807.000000	787.000000	3224.000000	1307.000000	324.000000

Filter Data by Night Traffic 7pm-7am

• There are 310256 out of 549976 records = 56.41% of dataset

Temporal Analysis:

1. Outline pedestrian counts during hourly intervals nighttime (7 PM to 6 AM).
2. Identify peak activity times.
3. Explore weekday & weekend traffic to understand different patterns.

# Convert datetime format
pedestrian_hour_sensor['timestamp'] = pd.to_datetime(pedestrian_hour_sensor['timestamp'])

# Set condition
pedestrian_hour_sensor['hour'] = pedestrian_hour_sensor['timestamp'].dt.hour
nighttime_data = pedestrian_hour_sensor[(pedestrian_hour_sensor['hour'] >= 19) | (pedestrian_hour_sensor['hour'] <= 7)]

# Display nighttime data
print(nighttime_data.head())

nighttime_data_len = len(nighttime_data)
print(f'The dataset contains {nighttime_data_len} records.')

  sensor_name                 timestamp  locationid  direction_1_x  \
3    Lat224_T 2023-05-02 20:00:00+00:00          62             50   
4    Lat224_T 2023-05-02 21:00:00+00:00          62             61   
5    Lat224_T 2023-05-02 23:00:00+00:00          62            106   
6    Lat224_T 2023-05-03 02:00:00+00:00          62            211   
7    Lat224_T 2023-05-03 03:00:00+00:00          62            191   

   direction_2_x  total_of_directions                  location_x  \
3             27                   77  -37.80996494, 144.96216521   
4             42                  103  -37.80996494, 144.96216521   
5             79                  185  -37.80996494, 144.96216521   
6            147                  358  -37.80996494, 144.96216521   
7            108                  299  -37.80996494, 144.96216521   

   location_id   sensor_description installation_date note location_type  \
3         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
4         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
5         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
6         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
7         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   

  status direction_1_y direction_2_y   latitude   longitude  \
3      A          East          West -37.809965  144.962165   
4      A          East          West -37.809965  144.962165   
5      A          East          West -37.809965  144.962165   
6      A          East          West -37.809965  144.962165   
7      A          East          West -37.809965  144.962165   

                   location_y  hour  
3  -37.80996494, 144.96216521    20  
4  -37.80996494, 144.96216521    21  
5  -37.80996494, 144.96216521    23  
6  -37.80996494, 144.96216521     2  
7  -37.80996494, 144.96216521     3  
The dataset contains 310256 records.

# Find heaviest traffic locations
# Group (location and hour) = total traffic
traffic_per_location_hour = nighttime_data.groupby(['location_x'])['total_of_directions'].sum().reset_index()

# Heaviest locations/ times
heaviest_locations_times = traffic_per_location_hour.sort_values(by='total_of_directions', ascending=False).head()
print(heaviest_locations_times)

                    location_x  total_of_directions
57  -37.81668634, 144.96689733              9228807
48   -37.81487988, 144.9660878              7709868
70  -37.81798049, 144.96503383              5817965
40  -37.81349441, 144.96515323              5736479
24  -37.81057846, 144.96444294              5673737

# Look at time sequence of data for ACMI Traffic NIGHT
specific_sensor_data_acmi_night = nighttime_data[nighttime_data['sensor_name'] == 'ACMI_T']
specific_sensor_data_acmi_night = specific_sensor_data_acmi_night.groupby('timestamp')['total_of_directions'].sum()

# Plot
plt.figure(figsize=(10, 5))
plt.plot(specific_sensor_data_acmi_night.index, specific_sensor_data_acmi_night.values, color=color_d[3], alpha=0.7)
plt.axhline(specific_sensor_data_acmi_night.mean(), color='purple', linestyle='--', label='Avg ACMI_T Traffic')
plt.title('Traffic Over Time for Sensor ACMI_T')
plt.xlabel('Time')
plt.ylabel('Total Traffic')
plt.grid(True)
plt.show()

specific_sensor_data_acmi_night.describe()

count    3731.000000
mean      535.459394
std       338.964905
min         5.000000
25%       279.000000
50%       525.000000
75%       748.500000
max      2543.000000
Name: total_of_directions, dtype: float64

# Group data (Sensor and timestamp + traffic)
grouped_data = nighttime_data.groupby(['sensor_name', 'timestamp'])['total_of_directions'].sum().unstack(0)

# Multiple sensors
plt.figure(figsize=(12, 7))
for sensor in grouped_data.columns:
    plt.plot(grouped_data.index, grouped_data[sensor], label=sensor)

plt.title('Traffic Over Time by Sensor')
plt.xlabel('Time')
plt.ylabel('Total Traffic')
plt.legend(title='Sensor Name', loc='upper left', bbox_to_anchor=(1.05, 1))

plt.grid(True)
plt.tight_layout()
plt.show()

# Remove NaN values
cleaned_nighttime_data = nighttime_data.dropna(subset=['latitude', 'longitude'])
mean_lat = cleaned_nighttime_data['latitude'].mean()
mean_lon = cleaned_nighttime_data['longitude'].mean()


map_folium = folium.Map(location=[mean_lat, mean_lon], zoom_start=13)
heat_data = [
    [row['latitude'], row['longitude'], row['total_of_directions']]
    for index, row in cleaned_nighttime_data.iterrows()
]

# HeatMap
HeatMap(heat_data, radius=15, max_zoom=13).add_to(map_folium)
map_folium.save('Nighttime_Pedestrian_Traffic.html')
map_folium

Make this Notebook Trusted to load map: File -> Trust Notebook

grouped_data.describe()

sensor_name	261Will_T	280Will_T	474Fl_T	488Mac_T	574Qub_T	ACMI_T	AG_T	AlfPl_T	BirBridge_T	BirFed1120_T	Bou231_T	Bou283_T	Bou292_T	Bou655_T	Bou688_T	Bou892T	BouBri_T	BouHbr2353_T	BouHbr_T	Boyd2837_T	Col12_T	Col15_T	Col254_T	Col620_T	Col623_T	Col700_T	Col892T	ElFi_T	Eli250_T	Eli263_T	Eli380_T	Eli483_T	Eli501_T	Errol20_T	Errol23_T	FLDegC_T	FLDegN_T	FLDegS_T	FedCycle_T	FedPed_T	Fli114C_T	Fli114F_T	FliSS_T	FliS_T	Fra118_T	Grat292_T	Hammer1584_T	HarEsB_T	HarEsP_T	KenMac_T	King2_T	King335_T	Lat224_T	LatWill_T	Lon189_T	Lon364_T	LtB170_T	LtB210_T	Lyg161_T	Lyg260_T	Lyg309_T	MCEC_T	NewQ_T	Pel147_T	PriNW_T	QVMQ_T	QVN_T	Que85_T	RMIT14_T	RMIT_T	Rus180_T	SanBri_T	Signal1647_T	SouthB_T	Spen161_T	Spen201_T	Spen229_T	Spr201_T	SprFli_T	Swa123_T	Swa148_T	Swa295_T	Swa31	Swa607_T	SwaCs_T	UM1_T	UM2_T	UM3_T	VAC_T	Vic_T	WatCit_T	WebBN_T	WestWP_T
count	3586.000000	3327.000000	1530.000000	3958.000000	3933.000000	3731.000000	4026.000000	3435.000000	2254.000000	1529.000000	3912.000000	3862.000000	3777.000000	275.000000	4022.000000	482.000000	2492.000000	1518.000000	4018.000000	1283.000000	3735.000000	4011.000000	4009.000000	3984.000000	3715.000000	3779.000000	921.000000	3766.000000	3722.000000	3717.000000	3054.000000	1481.000000	3517.000000	4023.000000	3990.000000	4003.000000	3939.000000	4035.000000	3260.000000	3262.000000	3842.000000	3965.000000	3916.000000	4035.000000	3668.000000	242.000000	1544.000000	3779.000000	3883.000000	3992.000000	1540.000000	1545.000000	4032.000000	3873.000000	3680.000000	3946.000000	3667.000000	3615.000000	3722.000000	2671.000000	3717.000000	2353.000000	4027.000000	3199.000000	3478.000000	3541.000000	3991.000000	3828.000000	3910.000000	4033.000000	3498.000000	3964.000000	1311.000000	3890.000000	1545.000000	1520.000000	1545.000000	3842.000000	3911.000000	3754.000000	3844.000000	4035.000000	4010.000000	3737.000000	3886.000000	1125.000000	2370.000000	2829.000000	3918.000000	3841.000000	3973.000000	3979.000000	3910.000000
mean	629.748187	201.645026	244.624183	132.501516	75.764048	535.459394	456.306508	191.729258	42.898846	246.925441	520.714980	944.864837	1518.792428	284.443636	909.681004	45.663900	626.301766	76.405138	144.150821	147.590803	352.106292	566.538519	854.022449	1168.702560	465.053836	766.144218	32.661238	1544.865905	1245.975819	489.076675	1239.858874	602.649561	334.457208	203.688292	156.185714	254.943542	277.417365	658.038414	103.372393	276.717964	37.036960	290.282724	806.263279	1118.902354	176.916303	272.528926	742.488990	92.705213	232.014164	94.951904	189.014935	213.370874	270.300099	298.086496	446.072011	350.760517	318.857649	597.624620	211.909995	121.311494	284.581921	962.633234	260.896697	131.709597	1508.672513	350.330133	1421.632924	302.190178	599.781841	1019.135383	564.717553	473.945005	131.681159	1300.905656	709.166990	619.333553	923.115858	318.114263	91.324469	2053.774108	865.913371	1405.671623	2301.448130	244.255285	749.660834	268.516444	216.841772	199.783669	1022.825676	189.714918	133.977599	304.007791	34.361893
std	544.441202	115.543745	122.390141	65.610870	42.733171	338.964905	410.223804	134.677347	57.937471	415.220644	313.594233	724.156548	1113.965827	206.029105	489.216999	34.788806	782.151896	67.192577	106.949289	62.097278	303.225704	411.828419	535.382931	663.258644	305.907573	794.422699	24.359497	844.738911	741.013040	294.992109	741.129674	398.789800	246.801118	118.161201	84.365990	161.887239	164.539900	351.485988	89.545951	474.080122	25.826181	187.411518	427.997479	646.674734	100.365119	296.367121	522.657156	84.378392	162.131461	48.879114	104.344411	105.686561	163.202178	203.678774	297.801102	212.805285	254.097162	466.046001	143.229281	92.904463	189.506062	627.780747	199.137259	98.332579	988.630444	252.217748	1033.494422	196.195153	400.429962	803.405590	391.390212	275.247562	92.613103	767.092085	365.422106	286.622676	335.823516	200.919768	55.714736	1354.066208	588.362919	978.025481	1199.987817	186.802965	477.294145	254.317045	216.812366	167.277220	697.702719	122.414057	97.497766	166.447969	26.063371
min	10.000000	2.000000	1.000000	1.000000	1.000000	5.000000	2.000000	1.000000	2.000000	1.000000	1.000000	4.000000	6.000000	2.000000	7.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	9.000000	3.000000	1.000000	1.000000	30.000000	23.000000	5.000000	43.000000	21.000000	12.000000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	12.000000	10.000000	2.000000	1.000000	2.000000	1.000000	1.000000	1.000000	2.000000	2.000000	4.000000	2.000000	6.000000	6.000000	2.000000	4.000000	1.000000	1.000000	1.000000	5.000000	1.000000	1.000000	22.000000	4.000000	8.000000	2.000000	8.000000	7.000000	7.000000	4.000000	2.000000	23.000000	15.000000	23.000000	18.000000	1.000000	1.000000	34.000000	2.000000	31.000000	55.000000	2.000000	5.000000	2.000000	1.000000	3.000000	7.000000	2.000000	1.000000	1.000000	1.000000
25%	224.000000	115.500000	171.000000	98.000000	42.000000	279.000000	178.000000	89.000000	4.000000	92.000000	279.000000	268.250000	408.000000	113.500000	648.000000	25.000000	193.750000	23.000000	64.000000	114.500000	104.000000	228.000000	343.000000	785.000000	255.000000	146.000000	14.000000	900.000000	629.000000	214.000000	588.000000	279.000000	141.000000	117.000000	98.000000	115.500000	146.000000	368.500000	50.000000	72.000000	20.000000	164.000000	465.000000	694.000000	98.000000	55.000000	356.500000	48.000000	136.000000	63.000000	99.750000	132.000000	144.750000	136.000000	181.000000	174.000000	92.000000	108.000000	97.000000	32.000000	106.000000	486.000000	140.000000	53.000000	713.250000	178.000000	399.500000	151.000000	269.000000	340.000000	192.000000	293.000000	71.000000	769.000000	426.000000	430.750000	775.000000	162.000000	59.000000	806.250000	345.750000	462.000000	1264.250000	88.000000	349.250000	64.000000	63.000000	75.000000	467.250000	105.000000	69.000000	202.000000	14.000000
50%	534.500000	213.000000	257.500000	140.000000	79.000000	525.000000	372.000000	176.000000	16.000000	146.000000	536.500000	964.500000	1660.000000	263.000000	899.000000	40.000000	460.500000	60.000000	126.000000	150.000000	339.000000	522.000000	925.000000	1126.500000	420.000000	556.000000	30.000000	1683.000000	1316.000000	513.000000	1240.500000	565.000000	278.000000	222.000000	166.000000	256.000000	295.000000	736.000000	74.000000	124.000000	34.000000	287.000000	859.000000	1108.000000	182.000000	128.000000	665.000000	83.000000	219.000000	103.000000	197.000000	232.000000	274.000000	269.000000	466.000000	372.000000	295.000000	650.000000	207.000000	107.000000	308.000000	905.000000	227.000000	116.000000	1450.500000	295.000000	1390.000000	296.000000	594.000000	942.000000	595.500000	455.000000	121.000000	1281.000000	766.000000	633.000000	968.000000	315.500000	87.000000	2341.500000	878.000000	1464.000000	2529.500000	223.000000	761.000000	186.000000	147.000000	162.000000	979.500000	178.000000	114.000000	310.000000	28.000000
75%	850.000000	279.000000	326.750000	179.000000	105.000000	748.500000	620.000000	263.000000	65.500000	274.000000	752.000000	1472.750000	2416.000000	433.500000	1187.750000	58.000000	801.000000	114.000000	211.000000	188.500000	478.500000	777.000000	1254.000000	1469.000000	621.500000	1064.500000	44.000000	2178.750000	1864.750000	739.000000	1844.750000	837.000000	462.000000	294.000000	211.000000	375.000000	384.000000	914.500000	124.000000	227.750000	49.000000	388.000000	1098.250000	1469.000000	245.000000	469.500000	1052.500000	122.000000	307.000000	131.000000	263.250000	287.000000	374.000000	423.000000	688.000000	512.000000	490.000000	986.000000	306.000000	190.000000	425.000000	1365.000000	347.000000	188.000000	2155.750000	480.000000	2315.000000	420.000000	818.000000	1370.000000	905.000000	628.000000	170.000000	1790.000000	924.000000	807.250000	1138.000000	449.000000	118.000000	3114.000000	1299.000000	2250.500000	3210.750000	346.000000	1063.000000	403.000000	286.000000	285.000000	1437.750000	252.000000	181.000000	412.000000	49.000000
max	2813.000000	609.000000	907.000000	340.000000	241.000000	2543.000000	4345.000000	890.000000	552.000000	5680.000000	1658.000000	3791.000000	5188.000000	1053.000000	2997.000000	274.000000	8716.000000	518.000000	1898.000000	316.000000	1676.000000	1866.000000	2368.000000	3518.000000	2595.000000	4137.000000	158.000000	3729.000000	3047.000000	1102.000000	3098.000000	1957.000000	1373.000000	560.000000	429.000000	829.000000	772.000000	1413.000000	548.000000	5251.000000	290.000000	1714.000000	2445.000000	7367.000000	495.000000	905.000000	6908.000000	1899.000000	1932.000000	255.000000	556.000000	534.000000	3692.000000	1051.000000	2792.000000	1061.000000	2241.000000	2135.000000	968.000000	527.000000	915.000000	4960.000000	2328.000000	519.000000	5614.000000	1873.000000	4132.000000	935.000000	3394.000000	5269.000000	2814.000000	2921.000000	541.000000	8526.000000	2735.000000	1771.000000	1870.000000	1184.000000	985.000000	5963.000000	3282.000000	4905.000000	5406.000000	1030.000000	3151.000000	1127.000000	1721.000000	1683.000000	4807.000000	743.000000	1427.000000	1055.000000	324.000000

# Mean traffic for each sensor
mean_traffic_per_sensor = grouped_data.mean()

# Traffic > 500
high_traffic_sensors = mean_traffic_per_sensor[mean_traffic_per_sensor > 1000]
print("Sensors with high average traffic:")
print(high_traffic_sensors)

# Traffic < 100
low_traffic_sensors = mean_traffic_per_sensor[mean_traffic_per_sensor < 100]
print("\nSensors with low average traffic:")
print(low_traffic_sensors)

Sensors with high average traffic:
sensor_name
Bou292_T    1518.792428
Col620_T    1168.702560
ElFi_T      1544.865905
Eli250_T    1245.975819
Eli380_T    1239.858874
FliS_T      1118.902354
PriNW_T     1508.672513
QVN_T       1421.632924
RMIT_T      1019.135383
SouthB_T    1300.905656
Swa123_T    2053.774108
Swa295_T    1405.671623
Swa31       2301.448130
VAC_T       1022.825676
dtype: float64

Sensors with low average traffic:
sensor_name
574Qub_T        75.764048
BirBridge_T     42.898846
Bou892T         45.663900
BouHbr2353_T    76.405138
Col892T         32.661238
Fli114C_T       37.036960
HarEsB_T        92.705213
KenMac_T        94.951904
SprFli_T        91.324469
WestWP_T        34.361893
dtype: float64

# Graph
for sensor_name in high_traffic_sensors.index:
    specific_sensor_data = nighttime_data[nighttime_data['sensor_name'] == sensor_name]
    specific_sensor_data = specific_sensor_data.groupby('timestamp')['total_of_directions'].sum()
    plt.figure(figsize=(10, 5))
    plt.plot(specific_sensor_data.index, specific_sensor_data.values, label=f'Sensor {sensor_name}', alpha=0.7)
    plt.axhline(specific_sensor_data.mean(), linestyle='--', color = 'purple', label=f'Avg {sensor_name} Traffic')
    plt.title(f'Traffic Over Time for Sensor {sensor_name}')
    plt.xlabel('Time')
    plt.ylabel('Total Traffic')
    plt.legend()
    plt.grid(True)
    plt.show()

# Graph
for sensor_name in low_traffic_sensors.index:
    specific_sensor_data = nighttime_data[nighttime_data['sensor_name'] == sensor_name]
    specific_sensor_data = specific_sensor_data.groupby('timestamp')['total_of_directions'].sum()
    plt.figure(figsize=(10, 5))
    plt.plot(specific_sensor_data.index, specific_sensor_data.values, label=f'Sensor {sensor_name}', alpha=0.7)
    plt.axhline(specific_sensor_data.mean(), linestyle='--', color = 'purple', label=f'Avg {sensor_name} Traffic')
    plt.title(f'Traffic Over Time for Sensor {sensor_name}')
    plt.xlabel('Time')
    plt.ylabel('Total Traffic')
    plt.legend()
    plt.grid(True)
    plt.show()

mean_traffic_per_sensor = grouped_data.mean(axis=0)  # Calculate rows
# Find top 5 locations
top_sensors = mean_traffic_per_sensor.sort_values(ascending=False).head(5)
print("NightTime Top 5 Traffic Areas:")
print(top_sensors)

NightTime Top 5 Traffic Areas:
sensor_name
Swa31       2301.448130
Swa123_T    2053.774108
ElFi_T      1544.865905
Bou292_T    1518.792428
PriNW_T     1508.672513
dtype: float64

# # Highest Traffic Areas
color_primary = color_d_extend

plt.figure(figsize=(14, 7))
color_primary_extended = color_primary * (len(top_sensors.index) // len(color_primary) + 1)
color_median_extended = color_median * (len(top_sensors.index) // len(color_median) + 1)

for sensor, color, color_med in zip(top_sensors.index, color_primary_extended, color_median_extended):
    # Plot sensor
    plt.plot(grouped_data.index, grouped_data[sensor], label=f'{sensor} Traffic', color=color, alpha=0.5)

    # Calculate median
    median_traffic = grouped_data[sensor].median()
    plt.axhline(y=median_traffic, color=color_med, linestyle='--', label=f'{sensor} Median')

plt.title('Time Sequence of Highest Traffic Areas')
plt.xlabel('Time')
plt.ylabel('Total Traffic')
plt.legend(title='Sensor Names', loc='upper left', bbox_to_anchor=(1.05, 1))
plt.grid(True)
plt.tight_layout()
plt.show()

# Find location for PM
def extract_lat_lon(location):
    lat, lon = location.split(', ')
    return float(lat), float(lon)

nighttime_data['latitude'], nighttime_data['longitude'] = zip(*nighttime_data['location_x'].apply(extract_lat_lon))
grouped_data = nighttime_data.groupby(['sensor_name', 'timestamp'])['total_of_directions'].sum().unstack(0)
mean_traffic_per_sensor = grouped_data.mean(axis=0)

# Find top 5 locations
top_sensors = mean_traffic_per_sensor.sort_values(ascending=False).head(5)
nighttime_map = folium.Map(location=[nighttime_data['latitude'].mean(), nighttime_data['longitude'].mean()], zoom_start=15)
for sensor in top_sensors.index:
    lat = nighttime_data.loc[nighttime_data['sensor_name'] == sensor, 'latitude'].iloc[0]
    lon = nighttime_data.loc[nighttime_data['sensor_name'] == sensor, 'longitude'].iloc[0]
    traffic = top_sensors[sensor]
    folium.Marker(location=[lat, lon], popup=f"Sensor: {sensor}\nTraffic: {traffic}").add_to(nighttime_map)

nighttime_map

Make this Notebook Trusted to load map: File -> Trust Notebook

# Find lowest 5 locations
lowest_sensors = mean_traffic_per_sensor.sort_values(ascending=True).head(5)
print("NightTime Top 5 Traffic Areas:")
print(lowest_sensors)

NightTime Top 5 Traffic Areas:
sensor_name
Col892T        32.661238
WestWP_T       34.361893
Fli114C_T      37.036960
BirBridge_T    42.898846
Bou892T        45.663900
dtype: float64

Outliers in dataset

• Replaced outliers with mean

# # Highest Traffic Areas
color_primary = color_d_extend

plt.figure(figsize=(14, 7))
color_primary_extended = color_primary * (len(lowest_sensors.index) // len(color_primary) + 1)
color_median_extended = color_median * (len(lowest_sensors.index) // len(color_median) + 1)

for sensor, color, color_med in zip(lowest_sensors.index, color_primary_extended, color_median_extended):
    # Plot sensor
    plt.plot(grouped_data.index, grouped_data[sensor], label=f'{sensor} Traffic', color=color, alpha=0.5)

    # Calculate median
    median_traffic = grouped_data[sensor].median()
    plt.axhline(y=median_traffic, color=color_med, linestyle='--', label=f'{sensor} Median')

plt.title('Time Sequence of Lowest Traffic Areas')
plt.xlabel('Time')
plt.ylabel('Total Traffic')
plt.legend(title='Sensor Names', loc='upper left', bbox_to_anchor=(1.05, 1))
plt.grid(True)
plt.tight_layout()
plt.show()

# Find lowest 5 locations
lowest_sensors = mean_traffic_per_sensor.sort_values(ascending=True).head(5)
nighttime_map = folium.Map(location=[nighttime_data['latitude'].mean(), nighttime_data['longitude'].mean()], zoom_start=15)
for sensor in lowest_sensors.index:
    lat = nighttime_data.loc[nighttime_data['sensor_name'] == sensor, 'latitude'].iloc[0]
    lon = nighttime_data.loc[nighttime_data['sensor_name'] == sensor, 'longitude'].iloc[0]
    traffic = lowest_sensors[sensor]
    folium.Marker(location=[lat, lon], popup=f"Sensor: {sensor}\nTraffic: {traffic}").add_to(nighttime_map)

nighttime_map

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nighttime_data.head()

	sensor_name	timestamp	locationid	direction_1_x	direction_2_x	total_of_directions	location_x	location_id	sensor_description	installation_date	note	location_type	status	direction_1_y	direction_2_y	latitude	longitude	location_y	hour
3	Lat224_T	2023-05-02 20:00:00+00:00	62	50	27	77	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	20
4	Lat224_T	2023-05-02 21:00:00+00:00	62	61	42	103	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	21
5	Lat224_T	2023-05-02 23:00:00+00:00	62	106	79	185	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	23
6	Lat224_T	2023-05-03 02:00:00+00:00	62	211	147	358	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	2
7	Lat224_T	2023-05-03 03:00:00+00:00	62	191	108	299	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	3

Identify peak activity times

• Find the hour(s) with the highest total activity, and then print and visualize the peak activity times.

# Plot top sensor locations heighest traffic
top_sensors_data = nighttime_data[nighttime_data['sensor_name'].isin(top_sensors.index)]
activity_by_sensor_and_hour = top_sensors_data.groupby(['sensor_name', 'hour'])['total_of_directions'].sum()
ordered_hours = list(range(19, 24)) + list(range(0, 8))
labels = [f"{h} ({(h if h < 12 else h-12) or 12}{'am' if h < 12 else 'pm'})" for h in ordered_hours]

plt.figure(figsize=(14, 7))
for sensor in top_sensors.index:
    hourly_data = activity_by_sensor_and_hour.loc[sensor].reindex(ordered_hours, fill_value=0)
    plt.plot(labels, hourly_data, marker='o', label=f"{sensor}")

plt.title('Nighttime Traffic Activity by Hour for Top 5 Sensors')
plt.xlabel('Hour of the Night')
plt.ylabel('Total Traffic')
plt.xticks(rotation=90)  # Rotate labels for better readability
plt.legend(title='Sensor')
plt.grid(True)
plt.show()

# Plot top sensor locations lowest traffic
bottom_sensors = mean_traffic_per_sensor.nsmallest(5)
bottom_sensors_data = nighttime_data[nighttime_data['sensor_name'].isin(bottom_sensors.index)]
activity_by_sensor_and_hour = bottom_sensors_data.groupby(['sensor_name', 'hour'])['total_of_directions'].sum()
ordered_hours = list(range(19, 24)) + list(range(0, 8))
labels = [f"{h} ({(h if h < 12 else h-12) or 12}{'am' if h < 12 else 'pm'})" for h in ordered_hours]

plt.figure(figsize=(14, 7))
for sensor in bottom_sensors.index:
    hourly_data = activity_by_sensor_and_hour.loc[sensor].reindex(ordered_hours, fill_value=0)
    plt.plot(labels, hourly_data, marker='o', label=f"{sensor}")

plt.title('Nighttime Traffic Activity by Hour for Bottom 5 Sensors')
plt.xlabel('Hour of the Night')
plt.ylabel('Total Traffic')
plt.xticks(rotation=90)
plt.legend(title='Sensor')
plt.grid(True)
plt.show()

Identify correlation between Traffic Sensors and Parking Data

• User can find nearby parking options
• Haversine formula

User interface - Locate nearest parking

# Find nearest parking location
def haversine(lat1, lon1, lat2, lon2):
    lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
    c = 2 * np.arcsin(np.sqrt(a))
    R = 6371.0  # Earth radius in kilometers
    distance_meters = R * c * 1000  # Convert to meters
    return distance_meters

# Nearest parking spot
def find_nearest_parking_sensor(traffic_sensor_location, parking_data):
    min_distance = float('inf')
    nearest_sensor = None

    for index, row in parking_data.iterrows():
        parking_lat = row['latitude']
        parking_lon = row['longitude']
        distance = haversine(traffic_sensor_location[0], traffic_sensor_location[1], parking_lat, parking_lon)
        if distance < min_distance:
            min_distance = distance
            nearest_sensor = row

    print(f"Distance to nearest parking sensor: {min_distance:.2f} meters")
    return nearest_sensor

# User input
lat = float(input("Enter latitude for the traffic sensor location: "))
lon = float(input("Enter longitude for the traffic sensor location: "))
traffic_sensor_location = (lat, lon)
nearest_parking_sensor = find_nearest_parking_sensor(traffic_sensor_location, on_street_parking)
print(nearest_parking_sensor)

# Example
# latitude                                                         -37.836245
# longitude                                                        144.982021

Enter latitude for the traffic sensor location: -37.836245
Enter longitude for the traffic sensor location: 144.982021
Distance to nearest parking sensor: 0.05 meters
roadsegmentid                                                         22730
roadsegmentdescription    Park Street between Mason Street and Randall P...
latitude                                                         -37.836245
longitude                                                        144.982021
lastupdated                                                      2022-08-31
Name: 15026, dtype: object

'\nlatitude                                                         -37.836245\nlongitude                                                        144.982021\n'

# Find Parking for 5 traffic areas
for sensor in top_sensors.index:
    traffic_sensor_location = (nighttime_data.loc[nighttime_data['sensor_name'] == sensor, 'latitude'].iloc[0],
                               nighttime_data.loc[nighttime_data['sensor_name'] == sensor, 'longitude'].iloc[0])

    nearest_parking_sensor = find_nearest_parking_sensor(traffic_sensor_location, on_street_parking)
    distance = haversine(traffic_sensor_location[0], traffic_sensor_location[1],
                         nearest_parking_sensor['latitude'], nearest_parking_sensor['longitude'])

    print(f"Distance from traffic sensor {sensor} to nearest parking sensor: {distance} meters")

Distance to nearest parking sensor: 22.89 meters
Distance from traffic sensor Swa31 to nearest parking sensor: 22.88627125361435 meters
Distance to nearest parking sensor: 38.35 meters
Distance from traffic sensor Swa123_T to nearest parking sensor: 38.351252140340954 meters
Distance to nearest parking sensor: 13.88 meters
Distance from traffic sensor ElFi_T to nearest parking sensor: 13.883197942752169 meters
Distance to nearest parking sensor: 97.73 meters
Distance from traffic sensor Bou292_T to nearest parking sensor: 97.73196675195793 meters
Distance to nearest parking sensor: 162.78 meters
Distance from traffic sensor PriNW_T to nearest parking sensor: 162.7780944031611 meters

Part 2

Spatial Analysis:

Map the pedestrian counts onto the city's pedestrian network to identify high and low traffic areas. Use machine learning clustering techniques to identify hotspots of night activity.

nighttime_data.dtypes

sensor_name                         object
timestamp              datetime64[ns, UTC]
locationid                           int64
direction_1_x                        int64
direction_2_x                        int64
total_of_directions                  int64
location_x                          object
location_id                        float64
sensor_description                  object
installation_date                   object
note                                object
location_type                       object
status                              object
direction_1_y                       object
direction_2_y                       object
latitude                           float64
longitude                          float64
location_y                          object
hour                                 int32
dtype: object

pedestrian_network.dtypes

geo_point_2d    object
geo_shape       object
objectid         int64
lat             object
lon             object
dtype: object

business_establishments.dtypes

census_year                       int64
block_id                          int64
property_id                       int64
base_property_id                  int64
clue_small_area                  object
trading_name                     object
business_address                 object
industry_anzsic4_code             int64
industry_anzsic4_description     object
longitude                       float64
latitude                        float64
dtype: object

Created GeoDataFrames

• Pedestrian network
• nightime_data (combined PM filtered data)

# GeoDataFrames
pedestrian_network['lat'] = pd.to_numeric(pedestrian_network['lat'], errors='coerce')
pedestrian_network['lon'] = pd.to_numeric(pedestrian_network['lon'], errors='coerce')
pedestrian_network = pedestrian_network.dropna(subset=['lat', 'lon'])

pedestrian_network_gdf = gpd.GeoDataFrame(pedestrian_network,
                                           geometry=gpd.points_from_xy(pedestrian_network['lon'],
                                                                       pedestrian_network['lat']),
                                           crs="EPSG:4326")

nighttime_data_gdf = gpd.GeoDataFrame(nighttime_data,
                                      geometry=gpd.points_from_xy(nighttime_data['longitude'],
                                                                  nighttime_data['latitude']),
                                      crs="EPSG:4326")

print("pedestrian_network_gdf head:")
print(pedestrian_network_gdf.head())
print("\nnighttime_data_gdf head:")
print(nighttime_data_gdf.head())

pedestrian_network_gdf head:
                          geo_point_2d  \
0  -37.791128630450004, 144.9250621319   
1    -37.790885197, 144.92478186034998   
2  -37.79063325525, 144.92244485650002   
3  -37.790601697949995, 144.9253671047   
4         -37.790968201, 144.942393422   

                                           geo_shape  objectid        lat  \
0  {"coordinates": [[144.9251354935, -37.79109699...      5735 -37.791129   
1  {"coordinates": [[144.9247703611, -37.79089170...      5738 -37.790885   
2  {"coordinates": [[144.9224965319, -37.79068385...      5740 -37.790633   
3  {"coordinates": [[144.9254894579, -37.79073841...      5743 -37.790602   
4  {"coordinates": [[144.9424411919, -37.79097389...      5744 -37.790968   

          lon                     geometry  
0  144.925062  POINT (144.92506 -37.79113)  
1  144.924782  POINT (144.92478 -37.79089)  
2  144.922445  POINT (144.92244 -37.79063)  
3  144.925367  POINT (144.92537 -37.79060)  
4  144.942393  POINT (144.94239 -37.79097)  

nighttime_data_gdf head:
  sensor_name                 timestamp  locationid  direction_1_x  \
3    Lat224_T 2023-05-02 20:00:00+00:00          62             50   
4    Lat224_T 2023-05-02 21:00:00+00:00          62             61   
5    Lat224_T 2023-05-02 23:00:00+00:00          62            106   
6    Lat224_T 2023-05-03 02:00:00+00:00          62            211   
7    Lat224_T 2023-05-03 03:00:00+00:00          62            191   

   direction_2_x  total_of_directions                  location_x  \
3             27                   77  -37.80996494, 144.96216521   
4             42                  103  -37.80996494, 144.96216521   
5             79                  185  -37.80996494, 144.96216521   
6            147                  358  -37.80996494, 144.96216521   
7            108                  299  -37.80996494, 144.96216521   

   location_id   sensor_description installation_date note location_type  \
3         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
4         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
5         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
6         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   
7         62.0  La Trobe St (North)        2019-09-25  NaN       Outdoor   

  status direction_1_y direction_2_y   latitude   longitude  \
3      A          East          West -37.809965  144.962165   
4      A          East          West -37.809965  144.962165   
5      A          East          West -37.809965  144.962165   
6      A          East          West -37.809965  144.962165   
7      A          East          West -37.809965  144.962165   

                   location_y  hour                     geometry  
3  -37.80996494, 144.96216521    20  POINT (144.96217 -37.80996)  
4  -37.80996494, 144.96216521    21  POINT (144.96217 -37.80996)  
5  -37.80996494, 144.96216521    23  POINT (144.96217 -37.80996)  
6  -37.80996494, 144.96216521     2  POINT (144.96217 -37.80996)  
7  -37.80996494, 144.96216521     3  POINT (144.96217 -37.80996)  

# Combine two GeoDataFrames
combined_gdf = gpd.GeoDataFrame(pd.concat([pedestrian_network_gdf, nighttime_data_gdf], ignore_index=True),
                                 crs="EPSG:4326")
print(combined_gdf.info())

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 395582 entries, 0 to 395581
Data columns (total 25 columns):
 #   Column               Non-Null Count   Dtype              
---  ------               --------------   -----              
 0   geo_point_2d         85326 non-null   object             
 1   geo_shape            85326 non-null   object             
 2   objectid             85326 non-null   float64            
 3   lat                  85326 non-null   float64            
 4   lon                  85326 non-null   float64            
 5   geometry             395582 non-null  geometry           
 6   sensor_name          310256 non-null  object             
 7   timestamp            310256 non-null  datetime64[ns, UTC]
 8   locationid           310256 non-null  float64            
 9   direction_1_x        310256 non-null  float64            
 10  direction_2_x        310256 non-null  float64            
 11  total_of_directions  310256 non-null  float64            
 12  location_x           310256 non-null  object             
 13  location_id          306568 non-null  float64            
 14  sensor_description   306568 non-null  object             
 15  installation_date    306568 non-null  object             
 16  note                 93527 non-null   object             
 17  location_type        306568 non-null  object             
 18  status               306568 non-null  object             
 19  direction_1_y        306568 non-null  object             
 20  direction_2_y        306568 non-null  object             
 21  latitude             310256 non-null  float64            
 22  longitude            310256 non-null  float64            
 23  location_y           306568 non-null  object             
 24  hour                 310256 non-null  float64            
dtypes: datetime64[ns, UTC](1), float64(11), geometry(1), object(12)
memory usage: 75.5+ MB
None

Trend Analysis:

• Yearly analysis to identify trends in nighttime foot traffic.

1. Find trends in the count of sensor data points over different years

# Find sensor yearly trends
combined_gdf['timestamp'] = pd.to_datetime(combined_gdf['timestamp'])
combined_gdf['month'] = combined_gdf['timestamp'].dt.month
combined_gdf['year'] = combined_gdf['timestamp'].dt.year

# Month vs Year
sensor_trends = combined_gdf.groupby(['year', 'month']).agg({
    'sensor_name': 'count',  # Counting the number of sensor data points
    'direction_1_x': 'mean', # Average of direction 1
    'direction_2_x': 'mean', # Average of direction 2
    'total_of_directions': 'sum' # Total directions
}).reset_index()

plt.figure(figsize=(12, 8))
plt.plot(sensor_trends['year'], sensor_trends['sensor_name'], marker='o', linestyle='-', label='Sensor Data Count', color=color_d[3])
plt.xlabel('Year')
plt.ylabel('Count')
plt.title('Trends in Sensor Data Counts over Years')
plt.legend()
plt.grid(True)
plt.show()

# Find location data based on year/ month (Lat and long & aggregate)
location_data = combined_gdf.groupby(['latitude', 'longitude', 'year', 'month']).size().reset_index(name='count')
plt.figure(figsize=(10, 6))
plt.scatter(location_data['longitude'], location_data['latitude'], s=location_data['count'], c=location_data['count'], cmap='viridis', alpha=0.5)
plt.colorbar(label='Count')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Trend Analysis based on Location Data')
plt.grid(True)
plt.show()

location_data.head()

	latitude	longitude	year	month	count
0	-37.825910	144.961860	2023.0	12.0	286
1	-37.825910	144.961860	2024.0	1.0	403
2	-37.825910	144.961860	2024.0	2.0	375
3	-37.825910	144.961860	2024.0	3.0	219
4	-37.824018	144.956044	2023.0	8.0	187

Find Traffic Data based on Year and Month

import pandas as pd
import matplotlib.pyplot as plt

# Collection started 2023 (03) thus remove 03 month as skews data
location_data = location_data[~((location_data['year'] == 2023) & (location_data['month'] == 3.0))]
monthly_traffic = location_data.groupby(['year', 'month']).agg({'count': 'sum'}).reset_index()
monthly_traffic['date'] = pd.to_datetime(monthly_traffic[['year', 'month']].assign(day=1))
yearly_traffic = location_data.groupby('year').agg({'count': 'sum'}).reset_index()

# Month
plt.figure(figsize=(12, 6))
plt.plot(monthly_traffic['date'], monthly_traffic['count'], marker='o')
plt.title('Monthly Nighttime Foot Traffic Trend')
plt.xlabel('Date')
plt.ylabel('Pedestrian Count')
plt.grid(True)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# data
display(monthly_traffic)
display(yearly_traffic)

	year	month	count	date
0	2023.0	4.0	28137	2023-04-01
1	2023.0	5.0	27140	2023-05-01
2	2023.0	6.0	28254	2023-06-01
3	2023.0	7.0	20386	2023-07-01
4	2023.0	8.0	26308	2023-08-01
5	2023.0	9.0	29944	2023-09-01
6	2023.0	10.0	2992	2023-10-01
7	2023.0	11.0	30757	2023-11-01
8	2023.0	12.0	30850	2023-12-01
9	2024.0	1.0	33213	2024-01-01
10	2024.0	2.0	32317	2024-02-01
11	2024.0	3.0	19597	2024-03-01

	year	count
0	2023.0	224768
1	2024.0	85127

# map
map_center = [combined_gdf['latitude'].mean(), combined_gdf['longitude'].mean()]
mymap = folium.Map(location=map_center, zoom_start=15)

# Traffic colours
blue_shades = {
    0: '#deebf7',    # Low traffic
    300: '#9ecae1',  # Moderate traffic
    800: '#3182bd',  # High traffic
    'max': '#08519c' # Highest traffic
}

# Map Markers
for index, row in location_data.iterrows():
    if row['count'] < 300:
        color = blue_shades[0]
    elif 300 <= row['count'] < 800:
        color = blue_shades[300]
    else:
        color = blue_shades[800]

    # Traffic intensity
    radius = 5 + 45 * (row['count'] / location_data['count'].max())
    folium.CircleMarker(
        location=[row['latitude'], row['longitude']],
        radius=radius,
        color=color,
        fill=True,
        fill_color=color,
        fill_opacity=0.6,
        popup=f"Count: {row['count']}"
    ).add_to(mymap)

mymap

Make this Notebook Trusted to load map: File -> Trust Notebook

on_street_parking.head()

	roadsegmentid	roadsegmentdescription	latitude	longitude	lastupdated
0	20751	Argyle Place South between Lygon Street and Ca...	-37.803448	144.965682	2023-05-28
1	20751	Argyle Place South between Lygon Street and Ca...	-37.803428	144.965496	2023-05-28
2	20751	Argyle Place South between Lygon Street and Ca...	-37.803413	144.965361	2023-05-28
3	22499	Queensberry Street between Lygon Street and Ca...	-37.804688	144.965314	2023-05-28
4	22499	Queensberry Street between Lygon Street and Ca...	-37.804695	144.965379	2023-05-28

User Interface: Find nearby parks based on sensor selected

• GeoDataFrame
• KDTree for fast nearest neighbor search

# User interface
# GeoDataFrame
on_street_parking_gdf = gpd.GeoDataFrame(
    on_street_parking,
    geometry=gpd.points_from_xy(on_street_parking.longitude, on_street_parking.latitude)
)

nighttime_data_gdf['geometry'] = gpd.points_from_xy(nighttime_data_gdf.longitude, nighttime_data_gdf.latitude)

# KDTree
parking_tree = cKDTree(on_street_parking_gdf[['longitude', 'latitude']])
sensor_tree = cKDTree(nighttime_data_gdf[['longitude', 'latitude']])
sensor_dropdown = Dropdown(options=nighttime_data_gdf['sensor_description'])

def plot_nearest_parking(sensor_description):
    selected_sensor = nighttime_data_gdf[nighttime_data_gdf['sensor_description'] == sensor_description].iloc[0]
    selected_sensor_location = (selected_sensor['longitude'], selected_sensor['latitude'])
    _, nearest_parking_indices = parking_tree.query(selected_sensor_location, k=5)
    m = folium.Map(location=[selected_sensor['latitude'], selected_sensor['longitude']], zoom_start=15)

    # Marker Location
    folium.Marker(
        location=[selected_sensor['latitude'], selected_sensor['longitude']],
        popup=f"Sensor: {sensor_description}",
        icon=folium.Icon(color='blue')
    ).add_to(m)

    # Markers Parking
    for idx in nearest_parking_indices:
        parking_location = on_street_parking_gdf.iloc[idx]
        folium.Marker(
            location=[parking_location['latitude'], parking_location['longitude']],
            popup=f"Parking: {parking_location['roadsegmentdescription']}",
            icon=folium.Icon(color='green')
        ).add_to(m)

    display(m)
interact(plot_nearest_parking, sensor_description=sensor_dropdown)

interactive(children=(Dropdown(description='sensor_description', options=('Spring St- Flinders st (West)', 'Sp…

plot_nearest_parking
def plot_nearest_parking(sensor_description)

/content/<ipython-input-124-14d41d18df05><no docstring>

Find businesses that are located in same area as top traffic sensors

• Find locations of the top sensors
• Filter heaviest traffic times for these sensors
• Identify businesses located near each top sensor
• Filter businesses based on times for peak traffic

Review business types

• Filter locations where business would benefit

Locations = [
    'Takeaway Food Services',
    'Entertainment Media Retailing',
    'Cafes and Restaurants',
    'Newspaper and Book Retailing',
    'Footwear Retailing',
    'Health and Fitness Centres and Gymnasia Operation',
    'Mens Clothing Retailing',
    'Womens Clothing Retailing',
    'Clothing Retailing',
    'Childrens Clothing Retailing',
    'Watch and Jewellery Retailing',
    'Hairdressing and Beauty Services',
    'Travel Agency and Tour Arrangement Services',
    'Flower Retailing',
    'Architectural Services',
    'Sport and Camping Equipment Retailing',
    'Pubs, Taverns, and Bars',
    'Mens Footwear Retailing',
    'Antique and Used Goods Retailing',
    'Department Stores',
    'Video and Other Electronic Media Rental',
    'Supermarket and Grocery Stores',
    'Liquor Retailing'
]

business_establishments.head()

	census_year	block_id	property_id	base_property_id	clue_small_area	trading_name	business_address	industry_anzsic4_code	industry_anzsic4_description	longitude	latitude
0	2004	44	108110	108110	Melbourne (CBD)	Total Tel International	Part Level 12, 140-0 Queen Street MELBOURNE 3000	5809	Other Telecommunications Services	144.961185	-37.815391
1	2004	44	108110	108110	Melbourne (CBD)	CJ Ham & Murray	Part Floor 2, 140 Queen Street MELBOURNE 3000	6720	Real Estate Services	144.961185	-37.815391
2	2004	44	108111	108111	Melbourne (CBD)	Netherlands Consulate	Suite 7, Part Level 4, 118-126 Queen Street ME...	7552	Foreign Government Representation	144.961308	-37.815656
3	2004	44	108111	108111	Melbourne (CBD)	Thomas Koutsoupias	Suite 10, Level 9, 118-126 Queen Street MELBOU...	6931	Legal Services	144.961308	-37.815656
4	2004	44	108111	108111	Melbourne (CBD)	K S T Partners	Suite 4, Floor 5, 118 Queen Street MELBOURNE 3000	6932	Accounting Services	144.961308	-37.815656

unique_records = business_establishments['industry_anzsic4_description'].unique()
print(unique_records)

['Other Telecommunications Services' 'Real Estate Services'
 'Foreign Government Representation' 'Legal Services'
 'Accounting Services' 'Alternative Health Services'
 'Office Administrative Services'
 'Computer System Design and Related Services'
 'Newspaper and Book Retailing' 'Other Specialised Design Services'
 'Houseware Retailing' 'Footwear Retailing' 'Health Insurance'
 'Health and Fitness Centres and Gymnasia Operation'
 'Technical and Vocational Education and Training'
 'Other Manufacturing n.e.c.' 'Other Interest Group Services n.e.c.'
 'Vacant Space' 'Hairdressing and Beauty Services'
 'Takeaway Food Services' 'Entertainment Media Retailing'
 'Pharmaceutical, Cosmetic and Toiletry Goods Retailing'
 'Mens Clothing Retailing' 'Cafes and Restaurants'
 'Womens Clothing Retailing' 'Clothing Retailing'
 'Childrens Clothing Retailing' 'Accommodation'
 'Watch and Jewellery Retailing' 'Other Repair and Maintenance n.e.c.'
 'Other Specialised Food Retailing'
 'Travel Agency and Tour Arrangement Services' 'Higher Education'
 'Other Administrative Services n.e.c.' 'Clothing and Footwear Repair'
 'Flower Retailing' 'Directory and Mailing List Publishing'
 'Jewellery and Silverware Manufacturing' 'Newspaper Publishing'
 'Religious Services' 'Other Store-Based Retailing n.e.c.'
 'Convenience Store' 'Architectural Services'
 'Management Advice and Other Consulting Services'
 'Other Gambling Activities' 'Fruit and Vegetable Retailing'
 'Credit Union Operation' 'Sport and Camping Equipment Retailing'
 'Employment Placement and Recruitment Services' 'Pubs, Taverns and Bars'
 'Parking Services' 'Mens Footwear Retailing'
 'Residential Property Operators' 'Clothing Manufacturing'
 'Optometry and Optical Dispensing' 'Antique and Used Goods Retailing'
 'Central Government Administration'
 'Business and Professional Association Services'
 'Engineering Design and Engineering Consulting Services'
 'Other Auxiliary Finance and Investment Services' 'Clubs (Hospitality)'
 'Electricity Transmission'
 'Internet Service Providers and Web Search Portals'
 'Other Specialised Industrial Machinery and Equipment Wholesaling'
 'Creative Artists, Musicians, Writers and Performers' 'Wool Wholesaling'
 'Clothing and Footwear Wholesaling' 'Water Freight Transport'
 'Electronic Information Storage Services'
 'Financial Asset Broking Services' 'State Government Administration'
 'On Selling Electricity and Electricity Market Operation'
 'Advertising Services' 'Book Publishing' 'Banking'
 'Photographic Film Processing' 'Grain-Sheep or Grain-Beef Cattle Farming'
 'Software Publishing' 'Land Development and Subdivision'
 'Adult, Community and Other Education n.e.c.'
 'Computer and Computer Peripheral Wholesaling' 'Superannuation Funds'
 'Scientific Testing and Analysis Services' 'Financial Asset Investing'
 'Surveying and Mapping Services' 'Museum Operation' 'Postal Services'
 'Electrical, Electronic and Gas Appliance Retailing'
 'Other Social Assistance Services' 'Other Personal Accessory Retailing'
 'Department Stores' 'Motion Picture Exhibition'
 'Video and Other Electronic Media Rental' 'Educational Support Services'
 'Amusement Parks and Centres Operation' 'Supermarket and Grocery Stores'
 'Other Water Transport Support Services'
 'Manchester and Other Textile Goods Retailing'
 'Motion Picture and Video Distribution' 'Toy and Game Retailing'
 'Other Personal Services n.e.c.' 'Liquor Retailing' 'Radio Broadcasting'
 'Dental Services' 'Paper Stationery Manufacturing'
 'Performing Arts Operation' 'Plumbing Good Wholesaling'
 'Performing Arts Venue Operation' 'Local Government Administration'
 'Justice' 'Wired Telecommunications Network Operation'
 'Other Agricultural Product Wholesaling'
 'Jewellery and Watch Wholesaling' 'Furniture Retailing'
 'Brothel Keeping and Prostitution Services' 'Specialist Medical Services'
 'Other Health Care Services n.e.c.' 'General Practice Medical Services'
 'Petroleum Product Wholesaling' 'Other Transport Support Services n.e.c.'
 'Other Allied Health Services' 'Special School Education'
 'Electronic (except Domestic Appliance) and Precision Equipment Repair and Maint'
 'Libraries and Archives' 'Motor Cycle Retailing'
 'Corporate Head Office Management Services' 'Womens Footwear Retailing'
 'Non-Residential Property Operators' 'Stationery Goods Retailing'
 'Magazine and Other Periodical Publishing'
 'Other Professional, Scientific and Technical Services n.e.c.'
 'Other Publishing (except Software, Music and Internet)'
 'Cereal Grain Wholesaling' 'General Insurance' 'Non-Depository Financing'
 'Scientific Research Services'
 'Bakery Product Manufacturing (Non-factory based)'
 'Fire Protection and Other Emergency Services'
 'Book and Magazine Wholesaling' 'Chiropractic and Osteopathic Services'
 'Sports and Physical Recreation Venues, Grounds and Facilities Operation'
 'Nature Reserves and Conservation Parks Operation'
 'Labour Association Services' 'Printing'
 'Computer and Computer Peripheral Retailing'
 'Passenger Car Rental and Hiring'
 'Fixed Space Heating, Cooling and Ventilation Equipment Manufacturing'
 'Car Retailing' 'Other Residential Building Construction'
 'Reproduction of Recorded Media' 'Arts Education'
 'Other Motor Vehicle and Transport Equipment Rental and Hiring'
 'Sport and Physical Recreation Administrative Service'
 'Other Automotive Repair and Maintenance' 'Freight Forwarding Services'
 'Liquor and Tobacco Product Wholesaling' 'Motor Vehicle Parts Retailing'
 'Building and Other Industrial Cleaning Services'
 'Other Warehousing and Storage Services'
 'Fresh Meat, Fish and Poultry Retailing'
 'Urban Bus Transport (Including Tramway)' 'Physiotherapy Services'
 'Casino Operation' 'Hospitals (except Psychiatric Hospitals)'
 'Other Fabricated Metal Product Manufacturing n.e.c.'
 'Laundry and Dry-Cleaning Services' 'Other Grocery Wholesaling'
 'Pharmaceutical and Toiletry Goods Wholesaling' 'Primary Education'
 'Automotive Electrical Services' 'Fuel Retailing'
 'Non-Residential Building Construction'
 'Pathology and Diagnostic Imaging Services'
 'Auxiliary Insurance Services'
 'Airport Operations and Other Air Transport Support Services'
 'Market Research and Statistical Services'
 'Investigation and Security Services' 'Child Care Services'
 'Textile Product Wholesaling' 'Floor Coverings Retailing'
 'Professional and Scientific Goods Wholesaling'
 'Toy and Sporting Goods Wholesaling' 'Ambulance Services'
 'Professional Photographic Services'
 'Cosmetic and Toiletry Preparation Manufacturing' 'Tyre Retailing'
 'Other Construction Services n.e.c.'
 'Industrial and Agricultural Chemical Product Wholesaling'
 'Furniture and Floor Covering Wholesaling' 'Air and Space Transport'
 'Other Agriculture and Fishing Support Services'
 'Cut and Sewn Textile Product Manufacturing'
 'Heavy Machinery and Scaffolding Rental and Hiring' 'Veterinary Services'
 'Machine Tool and Parts Manufacturing' 'Other Residential Care Services'
 'Other Electrical and Electronic Good Wholesaling'
 'Other Goods Wholesaling n.e.c.' 'Motor Vehicle New Part Wholesaling'
 'Automotive Body, Paint and Interior Repair'
 'Music and Other Sound Recording Activities'
 'Taxi and Other Road Transport' 'Iron Smelting and Steel Manufacturing'
 'Telecommunication Goods Wholesaling'
 'Other Wood Product Manufacturing n.e.c.'
 'Medical and Surgical Equipment Manufacturing'
 'Other Furniture Manufacturing' 'Other Ceramic Product Manufacturing'
 'Customs Agency Services'
 'Other Goods and Equipment Rental and Hiring n.e.c.'
 'Garden Supplies Retailing'
 'Wooden Structural Fitting and Component Manufacturing'
 'Printing Support Services' 'Catering Services' 'Other Metal Ore Mining'
 'Other Specialised Machinery and Equipment Manufacturing'
 'Motion Picture and Video Production' 'Painting and Decorating Services'
 'Courier Pick-up and Delivery Services'
 'Human Pharmaceutical and Medicinal Product Manufacturing'
 'Glass and Glass Product Manufacturing'
 'Post-production Services and Other Motion Picture and Video Activities'
 'Fish and Seafood Wholesaling' 'Car Wholesaling'
 'Trailer and Other Motor Vehicle Retailing' 'Secondary Education'
 'Other Electrical and Electronic Goods Retailing'
 'Other Hardware Good Wholesaling'
 'Polymer Film and Sheet Packaging Material Manufacturing'
 'Other Heavy and Civil Engineering Construction'
 'Pulp, Paper and Paperboard Manufacturing'
 'Grain Mill Product Manufacturing' 'Sugar Manufacturing'
 'Potato, Corn and Other Crisp Manufacturing'
 'Cake and Pastry Manufacturing (Factory based)'
 'Horse and Dog Racing Administration and Track Operation'
 'Other Horse and Dog Racing Activities'
 'Other Professional and Scientific Equipment Manufacturing'
 'Sports and Physical Recreation Clubs and Sports Professionals'
 'Wooden Furniture and Upholstered Seat Manufacturing'
 'Forestry Support Services' 'Free-to-Air Television Broadcasting'
 'Police Services' 'Gas Supply'
 'Computer and Electronic Office Equipment Manufacturing'
 'Commission-Based Wholesaling'
 'Other Depository Financial Intermediation'
 'Correctional and Detention Services' 'Timber Wholesaling'
 'Other Transport n.e.c.' 'Other Structural Metal Product Manufacturing'
 'Agricultural and Construction Machinery Wholesaling'
 'Mining and Construction Machinery Manufacturing'
 'Plaster Product Manufacturing' 'Labour Supply Services'
 'Cement and Lime Manufacturing'
 'Port and Water Transport Terminal Operations'
 'Paper Product Wholesaling' 'Iron Ore Mining'
 'Motor Vehicle Manufacturing' 'Communications Equipment Manufacturing'
 'Dairy Produce Wholesaling' 'Rope, Cordage and Twine Manufacturing'
 'Aircraft Manufacturing and Repair Services'
 'Sewerage and Drainage Services' 'Stevedoring Services'
 'Aged Care Residential Services' 'Psychiatric Hospitals'
 'Amusement and Other Recreational Activities n.e.c.'
 'Road Freight Transport' 'Fruit and Vegetable Processing'
 'Rail Freight Transport' 'Fruit and Vegetable Wholesaling'
 'Fire and Security Alarm Installation Services'
 'Landscape Construction Services' 'Metal and Mineral Wholesaling'
 'Hardware and Building Supplies Retailing' 'Rail Passenger Transport'
 'Interurban and Rural Bus Transport' 'Electricity Distribution'
 'Boatbuilding and Repair Services' 'Scenic and Sightseeing Transport'
 'Marine Equipment Retailing'
 'Non-Financial Intangible Assets (Except Copyrights) Leasing'
 'Other Machinery and Equipment Manufacturing n.e.c.'
 'Other Electrical Equipment Manufacturing'
 'Other Food Product Manufacturing n.e.c.' 'Other Information Services'
 'Other Machinery and Equipment Repair and Maintenance'
 'Basic Organic Chemical Manufacturing'
 'Architectural Aluminium Product Manufacturing' 'Electrical Services'
 'Rigid and Semi-Rigid Polymer Product Manufacturing' 'House Construction'
 'Cereal, Pasta and Baking Mix Manufacturing'
 'Zoological and Botanical Gardens Operation'
 'Sports and Physical Recreation Instruction'
 'Other Telecommunications Network Operation'
 'Other Mining Support Services' 'Mineral Exploration'
 'Internet Publishing and Broadcasting' 'Oil and Gas Extraction'
 'Metal Coating and Finishing' 'Other Electricity Generation'
 'Credit Reporting and Debt Collection Services'
 'Fossil Fuel Electricity Generation'
 'Other Non-Metallic Mineral Mining and Quarrying' 'Non-Store Retailing'
 'Call Centre Operation' 'Petroleum Exploration' 'Gold Ore Mining'
 'Data Processing and Web Hosting Services'
 'Cable and Other Subscription Broadcasting'
 'Document Preparation Services' 'Mineral Sand Mining'
 'Road and Bridge Construction' 'Fertiliser Manufacturing'
 'Ready-Mixed Concrete Manufacturing' 'Carpentry Services'
 'Seafood Processing' 'Meat Processing' 'Water Supply'
 'Other Machinery and Equipment Wholesaling n.e.c.'
 'Solid Waste Collection Services' 'Veneer and Plywood Manufacturing'
 'Beer Manufacturing' 'Water Passenger Transport'
 'Commercial Vehicle Wholesaling' 'Concreting Services'
 'Textile Floor Covering Manufacturing'
 'Other Electronic Equipment Manufacturing'
 'Soft Drink, Cordial and Syrup Manufacturing'
 'Other Domestic Appliance Manufacturing' 'Defence'
 'Cheese and Other Dairy Product Manufacturing' 'Packaging Services'
 'Meat, Poultry and Smallgoods Wholesaling' 'Confectionery Manufacturing'
 'Silver-Lead-Zinc Ore Mining' 'Other Construction Material Mining'
 'Pre-school Education' 'Combined Primary and Secondary Education'
 'Life Insurance' 'Funeral, Crematorium and Cemetery Services'
 'Natural Rubber Product Manufacturing'
 'Other Motor Vehicle Parts Manufacturing'
 'Prepared Animal and Bird Feed Manufacturing'
 'Bread Manufacturing (Factory based)'
 'Domestic Government Representation' 'Music Publishing'
 'Biscuit Manufacturing (Factory based)' 'Copper Ore Mining'
 'Diet and Weight Reduction Centre Operation'
 'Retail Commission-Based Buying and/or Selling'
 'Cleaning Compound Manufacturing'
 'Electric Lighting Equipment Manufacturing'
 'Other Building Installation Services' 'Tiling and Carpeting Services'
 'Roofing Services' 'Plumbing Services'
 'Structural Steel Erection Services' 'Kitchen and Diningware Wholesaling'
 'Cured Meat and Smallgoods Manufacturing' 'Natural Textile Manufacturing'
 'Toy, Sporting and Recreational Product Manufacturing'
 'Agricultural Machinery and Equipment Manufacturing' 'Glazing Services'
 'Automotive Electrical Component Manufacturing'
 'Concrete Product Manufacturing' 'Plastering and Ceiling Services'
 'Gardening Services'
 'Motor Vehicle Dismantling and Used Part Wholesaling'
 'Lifting and Material Handling Equipment Manufacturing'
 'Metal Furniture Manufacturing' 'Horse Farming'
 'Milk and Cream Processing' 'Pipeline Transport'
 'Railway Rolling Stock Manufacturing and Repair Services'
 'Air Conditioning and Heating Services' 'Grain Storage Services'
 'Waste Remediation and Materials Recovery Services' 'Regulatory Services'
 'Wine and Other Alcoholic Beverage Manufacturing' 'Central Banking'
 'Lottery Operation' 'Bauxite Mining'
 'Electric Cable and Wire Manufacturing'
 'Paint and Coatings Manufacturing' 'Nursery Production (Under Cover)'
 'Building Society Operation' 'Aluminium Smelting'
 'Other Waste Collection Services' 'Site Preparation Services'
 'Oil and Fat Manufacturing' 'Vegetable Growing (Under Cover)'
 'Photographic, Optical and Ophthalmic Equipment Manufacturing'
 'Basic Inorganic Chemical Manufacturing'
 'Hire of Construction Machinery with Operator'
 'Other Transport Equipment Manufacturing n.e.c.' 'Nickel Ore Mining'
 'Shipbuilding and Repair Services' 'Farm Animals and Bloodstock Leasing'
 'Petroleum Refining and Petroleum Fuel Manufacturing'
 'Textile Finishing and Other Textile Product Manufacturing'
 'Motor Vehicle Body and Trailer Manufacturing'
 'Other Non-Metallic Mineral Product Manufacturing' 'Forestry'
 'Prefabricated Wooden Building Manufacturing'
 'Hydro-Electricity Generation' 'Iron and Steel Casting'
 'Footwear Manufacturing' 'Other Sheet Metal Product Manufacturing'
 'Industrial Gas Manufacturing' 'Coal Mining'
 'Domestic Appliance Repair and Maintenance' 'Logging'
 'Structural Steel Fabricating' 'Waste Treatment and Disposal Services'
 'Other Public Order and Safety Services'
 'Reconstituted Wood Product Manufacturing' 'Pesticide Manufacturing'
 'Other Polymer Product Manufacturing' 'Other Grain Growing'
 'Turf Growing' 'Sheep-Beef Cattle Farming' 'Dairy Cattle Farming'
 'Trailer and Other Motor Vehicle Wholesaling'
 'Other Basic Chemical Product Manufacturing n.e.c.' 'Grape Growing'
 'Beef Cattle Farming (Specialised)'
 'Cigarette and Tobacco Product Manufacturing'
 'Leather Tanning, Fur Dressing and Leather Product Manufacturing'
 'Other Petroleum and Coal Product Manufacturing' 'Other Fishing'
 'Veterinary Pharmaceutical and Medicinal Product Manufacturing'
 'Pump and Compressor Manufacturing' 'General Line Grocery Wholesaling'
 'Poultry Processing' 'Other Converted Paper Product Manufacturing'
 'Other Basic Polymer Manufacturing' 'Clay Brick Manufacturing']

locations = [
    'Takeaway Food Services',
    'Entertainment Media Retailing',
    'Cafes and Restaurants',
    'Newspaper and Book Retailing',
    'Footwear Retailing',
    'Health and Fitness Centres and Gymnasia Operation',
    'Mens Clothing Retailing',
    'Womens Clothing Retailing',
    'Clothing Retailing',
    'Childrens Clothing Retailing',
    'Watch and Jewellery Retailing',
    'Hairdressing and Beauty Services',
    'Travel Agency and Tour Arrangement Services',
    'Flower Retailing',
    'Architectural Services',
    'Sport and Camping Equipment Retailing',
    'Pubs, Taverns, and Bars',
    'Mens Footwear Retailing',
    'Antique and Used Goods Retailing',
    'Department Stores',
    'Video and Other Electronic Media Rental',
    'Supermarket and Grocery Stores',
    'Liquor Retailing'
]

filtered_business_establishments = business_establishments[business_establishments['industry_anzsic4_description'].isin(locations)]

nighttime_data_gdf.head()

	sensor_name	timestamp	locationid	direction_1_x	direction_2_x	total_of_directions	location_x	location_id	sensor_description	installation_date	note	location_type	status	direction_1_y	direction_2_y	latitude	longitude	location_y	hour	geometry
3	Lat224_T	2023-05-02 20:00:00+00:00	62	50	27	77	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	20	POINT (144.96217 -37.80996)
4	Lat224_T	2023-05-02 21:00:00+00:00	62	61	42	103	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	21	POINT (144.96217 -37.80996)
5	Lat224_T	2023-05-02 23:00:00+00:00	62	106	79	185	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	23	POINT (144.96217 -37.80996)
6	Lat224_T	2023-05-03 02:00:00+00:00	62	211	147	358	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	2	POINT (144.96217 -37.80996)
7	Lat224_T	2023-05-03 03:00:00+00:00	62	191	108	299	-37.80996494, 144.96216521	62.0	La Trobe St (North)	2019-09-25	NaN	Outdoor	A	East	West	-37.809965	144.962165	-37.80996494, 144.96216521	3	POINT (144.96217 -37.80996)

Review top Traffic areas and businesses - Recommendation that these businesses stay open during PM traffic

• Filter the traffic data for the time range 7 PM to 7 AM
• Calculate mean traffic for each sensor
• Identify top 5 traffic sensors
• Find businesses within a certain radius of each top sensor (100 meters)

# Find top 5 locations (traffic) and match businesses
nighttime_data['timestamp'] = pd.to_datetime(nighttime_data['timestamp'])
nighttime_data['hour'] = nighttime_data['timestamp'].dt.hour
filtered_traffic_data = nighttime_data[(nighttime_data['hour'] >= 19) | (nighttime_data['hour'] <= 7)]
mean_traffic_per_sensor = filtered_traffic_data.groupby('sensor_name')['total_of_directions'].mean()
top_sensors = mean_traffic_per_sensor.nlargest(5)

def find_nearby_businesses(sensor_name, radius=100):
    sensor_location = nighttime_data[nighttime_data['sensor_name'] == sensor_name].iloc[0]
    sensor_lat, sensor_lon = sensor_location['latitude'], sensor_location['longitude']
    filtered_business_establishments['distance'] = np.sqrt(
        (filtered_business_establishments['latitude'] - sensor_lat) ** 2 +
        (filtered_business_establishments['longitude'] - sensor_lon) ** 2) * 111000 # Meters

    # Businesses filter distance
    nearby_businesses = filtered_business_establishments[filtered_business_establishments['distance'] <= radius]
    return nearby_businesses[['trading_name', 'business_address', 'industry_anzsic4_description', 'distance']]

results = []
for sensor in top_sensors.index:
    nearby_businesses = find_nearby_businesses(sensor)
    for _, business in nearby_businesses.iterrows():
        results.append({
            'Sensor Name': sensor,
            'Business Name': business['trading_name'],
            'Address': business['business_address'],
            'Industry': business['industry_anzsic4_description'],
            'Distance (m)': business['distance']
        })

results_df = pd.DataFrame(results)
print(results_df)

     Sensor Name                Business Name  \
0          Swa31                    The Store   
1          Swa31         Lee Tan Beauty Salon   
2          Swa31                    Whitemoss   
3          Swa31               Max Stern & Co   
4          Swa31  Kim Sing Chinese Restaurant   
...          ...                          ...   
4533     PriNW_T                Riverland Bar   
4534     PriNW_T                      Pilgrim   
4535     PriNW_T            Popstic Ice Cream   
4536     PriNW_T            Popstic Ice Cream   
4537     PriNW_T            Popstic Ice Cream   

                                             Address  \
0           Shop 1, 237 Flinders Lane MELBOURNE 3000   
1                   9-0 Flinders Lane MELBOURNE 3000   
2           Shop 1, 241 Flinders Lane MELBOURNE 3000   
3           Shop 20,  Flinders Street MELBOURNE 3000   
4           Shop 10,  Flinders Street MELBOURNE 3000   
...                                              ...   
4533         Vault , 1-9 Princes Walk MELBOURNE 3000   
4534       Vault , 15-19 Princes Walk MELBOURNE 3000   
4535   Vault , Part 1-19 Princes Walk MELBOURNE 3000   
4536   Vault , Part 1-19 Princes Walk MELBOURNE 3000   
4537  Part Vault 1-9 Princes Walk MELBOURNE VIC 3000   

                              Industry  Distance (m)  
0              Mens Clothing Retailing     54.529258  
1     Hairdressing and Beauty Services     68.747615  
2                     Flower Retailing     68.747615  
3     Antique and Used Goods Retailing     73.231126  
4                Cafes and Restaurants     73.231126  
...                                ...           ...  
4533             Cafes and Restaurants     86.568726  
4534             Cafes and Restaurants     86.568726  
4535            Takeaway Food Services     86.568726  
4536            Takeaway Food Services     87.599589  
4537            Takeaway Food Services     86.568726  

[4538 rows x 5 columns]

# Graph
plt.figure(figsize=(10, 6))
plt.bar(top_sensors.index, top_sensors.values, color=color_d[3])
plt.xlabel('Sensor Name')
plt.ylabel('Mean Traffic')
plt.title('Top 5 Traffic Sensors at Night')
plt.xticks(rotation=45)
plt.show()

# Find businesses correlate with top sensor
unique_business_names = results_df['Business Name'].unique()
print("Businesses within 100m Top Traffic Areas 6pm-6am:")
for name in unique_business_names:
    print(name)

Businesses within 100m Top Traffic Areas 6pm-6am:
The Store
Lee Tan Beauty Salon
Whitemoss
Max Stern & Co
Kim Sing Chinese Restaurant
Sushi Ten
Jolly J's
McDonald's
Retrostar Vintage Clothing
Daly Rudov Corporation Pty Limited
Collected Works
Hidden Secrets Tours
Lenko Boutique
Subway
Alphaville
Shuji Sushi
Tobu Sushi
C-Store
Best & Less Travel
Roule Galette
Alpha Barbers @ Flinders Lane
Flora Indian Restaurant & Cafe
Dangerfield
Swanston Halal Food Centre
Kuwaii
Vintage Sole
Marina Isles
Pollon Flowers
Chapter House Coffee
Ginger Olive Restaurant and Grill
Original Ugg
Krispy Kreme
Ben and Jerry's
Betula
Bobby's Cuts
Miss Lau
Flora Indian Restaurant
STA Travel
Flinders New and Original Clothing
Word bookstore Australia
Saffs Unisex Hairdressing
Peter Rowland at the Chapter House
Melbourne Halal Kebabs
Maya Tours & Travel
JNJ Australia Pty Ltd
Friends Travel
EB Games
Cubec
Life With Bird
Cafe On Flinders
Max Stern & Co Pty Ltd
Dangerfield Pty Ltd
McDonalds
KFC
Ms Sue Barnes
Drawing Initiatives
Mr Jamie Sormann
Dukes Coffee Roasters
DANIEL'S DONUTS
Yuii Nail Art Studio
Ben and Jerrys
SD Travel (Australia)
Keswick Bookshop
Huong
Mika Hair & Beauty Centre
Angus & Robertson
Linda Britten (studio Design)
Carrington Jewellers
Melbourne Sports Depot
Ivy Hopes
K H Edelstein Pty Ltd
Manchester Lane
C People
Fruitacious
Oshin Sushi Bar
Jesters The Jaffle Pie Co
LaVilla
Tha : Zone
Ali Baba
Sansal
Alice Euphemia
Children's Wear & Knitting Wool
Shoe Repair
City Vogue Shoe Covering
Saffs Hairdressing
Rice & Skinner
Subway Sandwich
Refectory 5 Pty Ltd
Genki
Joanna Nelson Architect
Caffe Moreno
Out of The Closet
Tourland Australia
Hungry Jack's
Imagination
Nest Architects
Phillip Ward Architects
Egotism Pty Ltd
Venus Boutique
Piccolo Cafe/bar
City Travel Centre
Bakpakker
Burger King Australia Pty Limited
Gasp Jeans
Yumton
Fresh Food Market Hawkers
Lady Petrova
Pollon
La Cassolette
Maggie Beauty House
Alice Nevins
Ratee Thai
Folk Architects
K.H. Edelstein Proprietary Limited
Shebeen
Yoli Frozen Yoghurt
Fame Agenda
White Moss
STA Travel Pty Ltd
In A Rush Espresso
Hungry Jack's Pty Ltd
Lederman's Authorised Newsagency
Urban Spaces Pty Ltd
Urban Creative
Little King Cafe
Journey By Nada Igual
Best and Less Travel
The Practical Man
Distal Phalanx
B3 Cafe et Patisserie
Foong And Sormann Pty Ltd
Sushi Hub Swanston
Hanul Tour
Ezymart
Croque Monsieur
Marc Dixon Architect
Alpha60
Rodd & Gunn
Drumplings
Cathedral Coffee
Little Mandarin Yoga
Nique
CBD Travel
Vasco Worldwide
Twist In Oz
Australia Shanghai Travel Pty Ltd
Krispy Kreme Australia Pty Ltd
Australia Shanghai Travel
Rice Design Pty Ltd
Dead & Buried
Cubec Clothing
Jealousy
Cafe On Flinders /Global Chat
Route 66
Lion In Love
World Food Books
Lang Walter Architecture
Koo & Son Pty Ltd
Bared Footwear
Lord of the Fries
Imagination World
Harry Wragg
Kisskill
Summerhill Lashes
La Kuaizi
Harold & Maude
The Greenpoint Group Pty Ltd
Euroka Coffee
Honya
Figure 8
Dirt Cheap CD's
Tober Sushi
G-Store
Liquid
Heart Baker Bun Mee
Little Mandarin Yoga & Pilates
Curly Siouxsie Hair
Lumi Clothing
Ava & Red
B3 Cafe
ROOPA PEMMARAJU
Sue Barnes Studio
Limedrop
OTC
City Convenience Store
Vera & Me
Lady
Youngs Kiosk
Obus
WhiteMoss Florist
Cafe on Flinders / Global Chat
Tasty Indian Restaurant
Jose Zarpan Exclusive Tailoring
Kuaizi Bar
Ben and Jerry
The Betula Store
Kanga Kanga
Ben & Jerry's
Formosa Place
Spunk Vintage Clothing
Richards Coffee Lounge
Nicks Wine Merchants (Melbourne)
Jesters Jaffle Pie
Nicks Wine Merchants
Skunkwear
Quicksilver Boardriders Club
Creative Hair Products
Bensons
Sunnina Jeweller
Paul Bram Jeweller
Falcone Hairdressing
Hugo Boss
Kintesu International Express
HI.S Australia
Bettina Liano
Glass Tree
Sidewalk Soul
French Connection
The Athletes Foot
The Eighth Floor
Wavelength For Hair
Cafe Meditteranean
Studio Girl
Clash City Cuts
Thai Viet (Kiosk)
Delicious Delights Cafe
Marc's
Andrew Trollope Architects
Arthur Galan
Tarocash
Australia China Travel Service
Cherry Love
Flowers Round The Hours
Ice
Salera's Jewellmasters
Willie Creek Pearls
Ten Ren's Tea Time
L'Incontro
Saba Melbourne
La Bella Dona
ZIPT Grooming for Men
Indigo
Dymocks
Stellini
Mellko
Minoos Laser & Beauty
Franco Jewellers
Thai Viet
Flinders Books
Crossways Food For Life
Mouche
Cherry Lane
Saba
Victorian Laser & Skin Clinic
Melko
James Alfredson
Runway Secrets
The Alley Edition
Minoo's Laser & Beauty Studio Pty Ltd
1932 Cafe & Restaurant
Merrell
Starbucks
Get Juicy
Dangerfield Clearance
La Petite Creperie
Suki Beauty
Tsujiri
Sophie Catherine Jewellery
H.I.S Travel Japan
Kafeteria
Cozi Hair On Collins
Femme Connection
Fast Times
Delhi Rocks
Toni Hair
Original Ugg Australia Classic
Creative Wigs
Yifang Taiwan Fruit Tea
Cheekee
Melbourne Institute of Nail & Beauty
HIS Travel
Pierucci
Rushka Espesso
Amazie International
Salon Golightly
Charles Rose
Switch Board Cafe
Direct Factory Outlets
Mittys' Newsagency
Foot Locker
Industrie
Gopal's Vegetarian Restaurant
Windsor Smith
Melbourne Opal Centre
La Bella Donna
H.I.S Australia Pty Ltd
Kintetsu International Express (Oceania) Pty Ltd
San Paolo Solarino
Caffe Mediterraneum
Simpsons the Jeweller
7 Angels
Alannah Hill
Politix
Peeptoe
Aust-China Travel Service Pty Ltd
Golden Tower Restaurant
EasyWay Tea
Voir Hair
Sushi Sushi
Circle of Food
Satch Clothing
Presgrave Place
Roun
Magi
Evita Jewellery
The Athletes Foot Australia Pty Ltd
Central Station Records
Alanah Hill
PSC Skateboarding
Duc Hairdressing
Sabi
Pilgrim
Falcone
Andrea Yasmin
Kiosk A
Sleepy Dogs Cafe
Andrew Cody Opals
Maestro Fashions
Designs & Beyond Architects
Flowers Round the Hours
Cucina Gran Turismo
Zamel's Jewellers
FCUK
Mitty's Newsagency
Little Screen DVD & Video Rental
Deborah K
Organic
Japan Airlines
Rich
Dust
Stilletto Hairdressing
Ghetto
Jacqui Fernandes
Malo 'e' Mala Pty Ltd
Jeans West
Watchmaker - Golden Square
Youthworks
Revival/Dangerfield
Smitten Kitten
Swatch
Central Station Records And Tapes
Design And Beyond Architects
Andrew Trollope
Lorna Jane
Ceasar's Hands
Gaura Travel Pty Ltd
Burlington Antiques
Bild Architecture Pty Ltd
Lo Me Shop
Platypus
Hannii
Bisonte
Hair Laboratory
Katsieris Origami Architects
Off Ya Tree
Yan Massage
Chatime
(No) Concept Store
Gorman
Pie Face 146 Swanston St
Skin Deep Beauty Therapy
French Pink
TLV Pop Up
Trewarne Fine Jewellery
Kerri Gravina
Collins Hair
Om Vegetarian
Smooth
7 Angles
H.I.S. Travel Japan
Miss Page Aveda Salon
Mai Yamauchi
Lightwell Studio
Tilly's Hair Boutique
Leopold
Delicious Delights
Pulp Kitchen
Golden Square Watchmaker & Jeweller
Hoss
Kesco
Lisa Barron
ZIPT Day Spa
Gold Blend Jewellers
Tomorrow Never Knows
K109 New Fashion
Gaura Travel
Ice Design
Ten Rens Tea Station
Easy Way Tea
Hanni
Pushka Espresso
Yan Beauty
Sushi Sushi (Melbourne) Pty Ltd
Dymocks (Storage)
Star Asian Grocery
Golden Square / Watchtower & Jewellery
Nevenka
Bild Art & Architecture
Morgan De Toi
Jalpak Travel
Zu
Miz Mila
Collins Nail Boutique
Curvy Design Shop
Union Kiosk
Trunk Tailors
The Distinctive Dame
Salon Bienvenue
Eclaire Beauty Studio
Minoo's Laser & Beauty Studio
Nail & Gel
Momo station
Kim Sing Restaurant
Alexander Urbanism Pty Ltd
Pure Wellness Studio
Puzzle Coffee
Yan Nails
Wendy Scully Millinery
Princes Pies
D'Artanian Dior Hairdressing
Designer Outlet
Bird of Prey
Hype DC
Luxm
Switchboard Barista
Fabrice Deville Pty Ltd
Rest & Relax
Jason Grech
Flinders Books / Kill City
Quiksilver Melbourne
Shophouse
Diamond Occasion
Cupp
Switchboard Cafe
Bell & Barnett
Yan Nails / Yan Massage
Harmony Massage
De Elegance
The Ugg Shop
Kerri Gravina Salon
Beauty Fairy Academy
Roxanne
30ml Coffee
Vico Beauty Boutique
Monsoon
Jacqui - E
YD
ZOE Tee's
JTB Australia Pty Limited
Meet Fresh
Toms
Unity Massage & Myotherapy
Maxi Cash
Paul Bram
Kickstw
Beauty Fairy
Holiday Xperience
Mon Amore Paris
Vanessa Lucas
Dr Martens
Crossways Vegetarian And Vegan Restaurant
Oxford
Marika Cut & Colour
Esther Weinberg Jewellery
Nail Studio M's Nail
La Peluqueria The Hair Salon
This Is Not A Toy Store
Chocolate Arcade
Ugg Since 1974
Schmik
Japan Airlines / Jalpak International Oceania
Youthworks Retail Co
Look Melbourne
Mooks
Louisa Boutique
Morrissey
Pepperoni's
City Pizza
Kimchi Tray
Esprit Haircare & Beauty
Design & Desire Fashion Jewellery
Travel House
Max Therapy
Golden Cup Australia Travel Pty Ltd
Liquorl Land Express
Spanish Doughnuts
Melbourne City Hair Supply
Bubble Cup Elizabeth Street
Hair 2 Go
Lord of The Fries Pty Ltd
Sam's IGA Plus Liquor
Yangcheng Travel Pty Ltd
Hella Good
Nando's
Peter Pan Adventure Travel
Ozyaki Melbourne
King Tea
Z Curry
Sushi Hub Elizabeth Street
Walker's Doughnuts
Anh's Artistry
Yarra Sushi
John Fluevog Shoes
Sine Qua Non Pty Ltd
Spanish Doughnuts El Churro Cafe
Grill'd
Flinders St Woolworths
Gloria Jean's Coffees
BARstudio
Dragonfly Discs
Mid City Newsagency
Vicki's Haircut Shop
B E Architecture
POD Architectural
Elizabeth Street Expresso
Cube Company
Spice of India
Fine Ground Cafe
Melbourne College of Hair And Beauty
La Citta
Nila
Lace Boutique
Elements Beauty/Body Bronze
Kimchi Tray Korean Cuisine
Helen Hair & Beauty
Leonard St
Migo's
Coles Central
Liquorland Express
Cone Heads
The Little Bookroom
Wicked Weasel
Sea Salt
Lacitta
Duncans
Cafe Sehion
V & J Menswear
Sal's Authentic New York Pizza
Flywheel Artisan Goods
Tulip Coffee
Clementines
Two Way Cafe
Melbourne Discount Books
Peter Christoff
The Mitey Bite
Minifie Nixon
Cunsolo Architects
City Basement Books
Coles Express
Waratah Florist
Mekina & Associates Travel Agents
Polish Information & Service Centre
Senior Arts Supplies
Cae Cafe Melbourne
KImchi Tray
M & A's Sushi & Smoothy Bar
IS Wright
Dragon China Tours & Travel
Liberated Bookshop
Vicki's Haircuts
N M B W
Degraves Espresso
Oculus Landscape Architecture Urban Design Pty Ltd
Robert Pierce & Associates
LibeRated Bookshop
Mr Jonathon Boucher / Broderick Ely
NMBW Architecture Studio
Kickz 101
Kenwood Electronics
Box Coffee
CDF Travel
Zomp Shoez
East of India
Esprit Hair & Beauty
Gailard & Trumble
Flinders St Coffee
Snack Delight
Mad Mex Flinders Lane
Vita Juice Energy For Life
Whiteroom Tours
Dizingof
What's Up Hair
Kikoo Sushi
QI Chinese Massage
South Melbourne Market Dim Sim
Fitness First Platinum
Persian Grill
Tinson Jewellers
Coles Elizabeth St
Platform 270
Openwork Pty Ltd
Shujinko Flinders
Rocket Burgers & Fries
Iscream
Pho Flinders Express
Happy Travels
Joyci Sushi
Tracy's Banh Mi
The Pole Room
Oz Travel Shoppe Pty Ltd
Blasphemy
Souvlakihut
Blacket Architects
Bluesea Migration And Education
Old Beijing Dumpling Restaurant
Sole Devotion
Panzerotti Di Cerignola
Zomp
Fine Grind Cafe
Curry Bar
Speed Boy Girl
Flywheel Artisan Meat and Baguette
Desyne Developments Pty Ltd
Pie Face
Metro Baguette
Black Velvet Elizabeth
Australian Ugg Boots
Aery Salon
Melbourne Halal Food Centre
Il Tempo 2 Pasta
Molecule Studio Pty Ltd
Concept Design
Michael Rahill
Ruby Rose Jewellry
Sime Qua Non
Groundshot
Oshin Sushi Bar Pty Ltd
Redline
Speed boy girl
Backpackers World
Melbourne City of Hair And Beauty
Red Nine
IGA Plus Liquor
Old Durbar Flinders
Osaka Bench
Coco Fresh Tea & Juice
BWS
Sine Qua Non
Cool Juice
Mat Architecture & Design
Robert Pierce & Michael Rahill Architecture
Cafe E S E
Hylands Books
Yangcheng Travel
Peter Christoff Architects
Nila Restaurant
Pole Princess CBD
Foodao
Classic Holidays
Thomas Findlay & Sons
Molecule Studio
Yoli Frozen Yogurt
Qi Chinese Massage
Pacific Green Tours
Wayne Finschi Concept Design
Shoo Biz
Matrix
@n Experience Nails & Beauty
H M V Music Shop
High And Mighty Big And Tall
JJ'S Cafe
Dotti
Supre
Kartyer
David Jones
Abbey Bridal Discounters Melbourne
Bevilles Jewellers
Allan Pinkus Shoes
Ong International Food Court
Eurasia Travel
Navi Tour
Consolidated Travel
Crown Cafe Bakery
Gogo Sushi
Matrix Pilates & Yoga
Pumpkin Patch
Sportsgirl
The Pancake Parlour
Jetty Surf
Adidas
Go Healthy Naturally Roasted Chestnuts / Nutgrove
Supre Pty Ltd
Bradmans Kiosk
Sports Junction
DM Jewellery Co
Claypot King
Sushi Jin
Starbucks Coffee
Purely Simple & Natural
Rubi Shoes
Swanston Walk Cafe Bar
Footlocker Australia
U & I Australia
Alan Pinkus Shoes
Rip Curl
Lyons Architects
Reader's Feast
DM Jewellery
Michael Hill
An Experience Beauty Clinic
Joe Black
Swarovski
The Louden Kitchen & Grill
Sneakerboy
Consolidated Travel Pty Ltd
The Tasty Spicy Hot Pot
Zara
MVS Architects
Soul Cafe
Sam Sam
Zao Buer BBQ & Bar
Melbourne Dae Jang Geum Korean BBQ
Rebel Sport
U&I Global
Pancake Parlour
Minifie Nixon Architects
Cue
Diva
Zara Melbourne
Minifie Van Schaik Pty Ltd
B & M Le Beau Monde
Consolidated Travel Pty Ltd / ir Mauritius
Mister Close
Gogo Kaiten Sushi
Centrepoint Coffee Lounge
Rebel Sports
Prouds The Jewellers
Puzsar Jeweller
Diamond Coffee Lounge
Kondo Tricot
Due Fratelli
DC Street
Abbess Opal Mine
Silky's
Emporio
Book City
Millers Fashion Club
Thomas Cook Travel
Katies
Yoyogi Japanese Cafe
T-Bar
Ugg Melbourne
Pad Thai
Air New Zealand
U&I Australia
Scapesystems
Rip Curl Pty Ltd
Wmk Architecture Pty Ltd
QUA
Review
Aquila
Senol Korkmaz
Squillace
D M Jewellery Co
General Pants Co
Navitour
Jetset Travelworld Ltd
Donut King
Aru Melbourne
Factorie
Rozzi's
Jag
AAT Kings
Coles Myer Ltd
Gray Line
Swish Clothing
Steve Madden
HBO & EMTB (Victoria) Pty Ltd
CUE
Adsix Kitchen & Bar
Seiko Boutique
Australian Diamond Company
Pandora
Da Hu the Great Tiger
T & Mirror
Qantas Business Travel
Holiday Xp (Mel Cbd)
Crown Cafe
Air Mauritius Ltd
Anne Rossi Korean Boutique
High and Mighty Big and Tall
Irregular Choice
Red Piggy
Superhiro
Surf Dive N Ski
Liho Curry & Noodle
Asian Gourmet
Basketball Land
Pink The Restaurant
Air Mauritius
Ly Rossi
Cheep Clothing
Red Duck Cafe
Instea
Fat Oma
Matrix Pilates & Yoga Studio
APT
Impex International Travel
Miller's Fashion Club (Vic) Pty Ltd
Chow Down
Riverland Bar
Pilgrim Bar
Six Degrees
Popstic Ice Cream

print(filtered_business_establishments.columns)

Index(['census_year', 'block_id', 'property_id', 'base_property_id',
       'clue_small_area', 'trading_name', 'business_address',
       'industry_anzsic4_code', 'industry_anzsic4_description', 'longitude',
       'latitude', 'distance'],
      dtype='object')

# Map
map_center = [combined_gdf['latitude'].mean(), combined_gdf['longitude'].mean()]
map_businesses = folium.Map(location=map_center, zoom_start=15)

# Match unique business names to get area
for name in unique_business_names:
    business_data = filtered_business_establishments[filtered_business_establishments['trading_name'] == name].iloc[0]
    business_lat, business_lon = business_data['latitude'], business_data['longitude']
    folium.Marker(location=[business_lat, business_lon], popup=name).add_to(map_businesses)

map_businesses

Make this Notebook Trusted to load map: File -> Trust Notebook

User Interface: Find nearby businesses based on sensor selected

• GeoDataFrame
• KDTree for fast nearest neighbor search

# User interface
filtered_business_establishments_gdf = gpd.GeoDataFrame(
    filtered_business_establishments,
    geometry=gpd.points_from_xy(filtered_business_establishments.longitude, filtered_business_establishments.latitude)
)

# KDTree
business_tree = cKDTree(filtered_business_establishments_gdf[['longitude', 'latitude']])
nighttime_data_gdf['geometry'] = gpd.points_from_xy(nighttime_data_gdf.longitude, nighttime_data_gdf.latitude)
sensor_dropdown = Dropdown(options=nighttime_data_gdf['sensor_description'].unique())
business_type_dropdown = Dropdown(options=locations)

def plot_nearest_businesses(sensor_description, business_type):
    selected_type_businesses = filtered_business_establishments_gdf[filtered_business_establishments_gdf['industry_anzsic4_description'] == business_type]
    type_business_tree = cKDTree(selected_type_businesses[['longitude', 'latitude']])
    selected_sensor = nighttime_data_gdf[nighttime_data_gdf['sensor_description'] == sensor_description].iloc[0]
    selected_sensor_location = (selected_sensor['longitude'], selected_sensor['latitude'])
    _, nearest_business_indices = type_business_tree.query(selected_sensor_location, k=5)
    m = folium.Map(location=[selected_sensor['latitude'], selected_sensor['longitude']], zoom_start=15)
    markers = MarkerCluster().add_to(m) # Clusters

    # Marker Location
    folium.Marker(
        location=[selected_sensor['latitude'], selected_sensor['longitude']],
        popup=f"Sensor: {sensor_description}",
        icon=folium.Icon(color='red', icon='info-sign')
    ).add_to(markers)

    # Markers Businesses
    for idx in nearest_business_indices:
        business = selected_type_businesses.iloc[idx]
        popup_text = f"Business: {business['trading_name']}\nAddress: {business['business_address']}\nIndustry: {business['industry_anzsic4_description']}"
        folium.Marker(
            location=[business['latitude'], business['longitude']],
            popup=popup_text,
            icon=folium.Icon(color='green', icon='ok-sign')
        ).add_to(markers)

    display(m)

# Button for update
button = Button(description="Update Map")
interact_manual(plot_nearest_businesses, sensor_description=sensor_dropdown, business_type=business_type_dropdown)

interactive(children=(Dropdown(description='sensor_description', options=('La Trobe St (North)', '231 Bourke S…

plot_nearest_businesses
def plot_nearest_businesses(sensor_description, business_type)

/content/<ipython-input-100-c3605dc7ec8e><no docstring>